Inoculating Potato Plantlets with Totontepec maize aerial root mucilage isolates
Shawn Higdon
library(lsmeans)
library(tidyverse)Read in the Datasets
set_1 <- read.csv("./all_potato_data/Set1-Table 1.csv", header = T)
set_2.1 <- read.csv("./all_potato_data/Set2_Day1-Table 1.csv", header = T)
set_2.2 <- read.csv("./all_potato_data/Set2_Day2-Table 1.csv", header = T)
set_2.3 <- read.csv("./all_potato_data/Set2_Day3-Table 1.csv", header = T)
set_2.4 <- read.csv("./all_potato_data/Set2_Day4-Table 1.csv", header = T)
set_3.1 <- read.csv("./all_potato_data/Set3_Day1-Table 1.csv", header = T)
set_3.2 <- read.csv("./all_potato_data/Set3_Day2-Table 1.csv", header = T)
set_3.3 <- read.csv("./all_potato_data/Set3_Day3-Table 1.csv", header = T)
set_3.4 <- read.csv("./all_potato_data/Set3_Day4-Table 1.csv", header = T)
set_3.5 <- read.csv("./all_potato_data/Set3_Day5-Table 1.csv", header = T)
set_4.1 <- read.csv("./all_potato_data/Set4_Day1-Table 1.csv", header = T)
set_4.2 <- read.csv("./all_potato_data/Set4_Day2-Table 1.csv", header = T)
set_4.3 <- read.csv("./all_potato_data/Set4_Day3-Table 1.csv", header = T)
# combine all datasets
master_set <- rbind(set_1, set_2.1, set_2.2, set_2.3, set_2.4, set_3.1, set_3.2, set_3.3, set_3.4, set_3.5, set_4.1, set_4.2, set_4.3)Create list of isolates
potato_isolate_list <- as_tibble(unique(master_set$isolate)) # grab unique isolate IDs
names(potato_isolate_list) <- "Isolate" # change variable name
str(potato_isolate_list)tibble [383 × 1] (S3: tbl_df/tbl/data.frame)
$ Isolate: chr [1:383] "200488" "201859" "201813" "201880" ...potato_isolate_list <- potato_isolate_list %>%
mutate_if(is.factor, na_if, y = "") # add NA to empty values
potato_isolate_list <- potato_isolate_list %>%
filter(grepl("2", Isolate)) # remove rows without '2' prefix (non-isolate rows)
potato_isolate_list$Isolate <- paste0('BCW-', potato_isolate_list$Isolate) # add BCW- prefix
potato_isolate_list <- arrange(potato_isolate_list, Isolate) # arrange in descending order
write_csv(potato_isolate_list, "./R_Output_Files/potato_isolate_list.csv", col_names = T)Statistical Analysis
Experimental Design
- Potato plantlets purchased from Canada in bulk were individually transplanted into 1X MS media slants.
- Mucilage Isolates were cultured in Luria’s Broth (LB) to an OD600 value of 0.6.
- 100 µL of each inoculum was applied to a plantlet vial, was sealed, and the vials were incubated at 28 ºC for 3 weeks.
- Each inoculation was evaluated in triplicate.
- All Isolates that were incorporated into the whole genome sequencing study were assessed with this assay.
- The number of isolates to be screened led to the full list being assessed in batches:
- Batch 1
- Batch 2 (4 groups)
- Batch 3 (5 groups)
- Batch 4 (3 groups)
Linear Modeling
Data measurements were fit to a linear model using the lm function of R. The normality of the data was assessed by visualizing parameters of each linear model in two ways:
- Normal Q-Q plots (Standardized residuals vs. Theoretical Quantiles)
- Scale - Location plots (Squareroot of Standard residuals vs. fitted values)
Anova
One way analysis of variance was applied to each linear model as a preliminary assessment of each inoculation batch.
The LSMeans Package
The LSMeans Package was used to calculate mean estimates, confidence intervals, and conduct multiple t-tests. Correction for multiple testing was implemented using either the Dunnett method, or the Tukey method (depending on the type of comparisons), and the adjusted p-values are presented in the outputs.
Compact Letter Display
Compact Letter Display is a function in R that assigns a common label to each treatment group based on whether or not the confidence intervals of the mean estimations from LSMeans overlap with those of other treatment groups. If two treatment groups have non-overlapping confidence intervals associated with their mean estimates, they are not classified within the same group. This allows higher security in asserting that the mean estimates are truly different at the specified confidence level.
Batch 1
# analyze set 1 data
str(set_1)'data.frame': 744 obs. of 4 variables:
$ isolate: chr "200488" "200488" "200488" "200488" ...
$ rep : int 1 1 1 1 2 2 2 2 3 3 ...
$ sample : chr "WS" "WR" "DS" "DR" ...
$ mg : num 152.2 38.5 10.4 3.4 182.4 ...#subset by sample type
set_1_DR <- filter(set_1, sample == "DR")
set_1_DS <- filter(set_1, sample == "DS")
set_1_WR <- filter(set_1, sample == "WR")
set_1_WS <- filter(set_1, sample == "WS")Experimental Design Table
| Structure | Factor | Type | # levels |
|---|---|---|---|
| Treatment | Isolate | Qualitative | 59 |
| Design | MS Media Slants | Experimental Unit | 177 |
| Response | Plant Tissue Weight | Observational Unit | 708 |
| Response | Dry Root Weight (DR) | Variable | 177 |
| Response | Dry Shoot Weight (DS) | Variable | 177 |
| Response | Wet Root Weight (WR) | Variable | 177 |
| Response | Wet Shoot Weight (WS) | Variable | 177 |
- 57 Isolates
- 2 controls
- control_W - 100 µL of saline solution
- control-WO - no added solution
1.1. Dry Root Weight
# linear model of dry root data
lm_set_1_DR <- lm(mg ~ 1 + isolate, set_1_DR)
op = par(mfrow=c(1,2))
plot(lm_set_1_DR, which = c(2,3))not plotting observations with leverage one:
35, 65, 69, 70par(op)
1.1. - Anova
anova(lm_set_1_DR)Analysis of Variance Table
Response: mg
Df Sum Sq Mean Sq F value Pr(>F)
isolate 45 411.56 9.1457 1.8049 0.01044 *
Residuals 81 410.43 5.0670
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 11.1. - LSMeans (Control_W) - Dunnett
lsm_s1.dun.DR <- summary(lsmeans(lm_set_1_DR, trt.vs.ctrl ~isolate, ref=45)$contrasts, infer = c(T,T))
write.csv(lsm_s1.dun.DR, "./lsmeans_summary_tables/lsm_s1.dun.DR.csv")1.1. - LSMeans (Control-WO) - Dunnett
summary(lsmeans(lm_set_1_DR, trt.vs.ctrl ~isolate, ref=46)$contrasts, infer = c(T,T)) contrast estimate SE df lower.CL upper.CL t.ratio p.value
200270 - control-WO -0.383 1.84 81 -6.37 5.603 -0.209 1.0000
200275 - control-WO 1.200 1.59 81 -3.98 6.384 0.754 0.9990
200444 - control-WO -3.333 2.43 81 -11.25 4.586 -1.371 0.9340
200488 - control-WO -3.767 1.59 81 -8.95 1.418 -2.366 0.3650
200496 - control-WO -2.200 1.59 81 -7.38 2.984 -1.382 0.9308
200533 - control-WO 2.717 1.84 81 -3.27 8.703 1.478 0.8991
200556 - control-WO -1.567 1.59 81 -6.75 3.618 -0.984 0.9926
200659 - control-WO -4.100 1.59 81 -9.28 1.084 -2.576 0.2499
200661 - control-WO -2.033 1.59 81 -7.22 3.151 -1.277 0.9569
200715 - control-WO -4.233 2.43 81 -12.15 3.686 -1.741 0.7734
200725 - control-WO -1.833 1.84 81 -7.82 4.153 -0.997 0.9918
200726 - control-WO -3.200 1.59 81 -8.38 1.984 -2.010 0.6009
200727 - control-WO -0.233 1.84 81 -6.22 5.753 -0.127 1.0000
200808 - control-WO -1.600 1.59 81 -6.78 3.584 -1.005 0.9914
200902 - control-WO -1.600 1.59 81 -6.78 3.584 -1.005 0.9914
200939 - control-WO -2.100 1.59 81 -7.28 3.084 -1.319 0.9475
201025 - control-WO -2.500 1.59 81 -7.68 2.684 -1.571 0.8612
201045 - control-WO -0.800 1.59 81 -5.98 4.384 -0.503 1.0000
201084 - control-WO -4.867 1.59 81 -10.05 0.318 -3.058 0.0845
201085 - control-WO -1.833 2.43 81 -9.75 6.086 -0.754 0.9990
201153 - control-WO -2.533 1.59 81 -7.72 2.651 -1.592 0.8516
201162 - control-WO -3.167 1.59 81 -8.35 2.018 -1.989 0.6152
201173 - control-WO -4.800 1.59 81 -9.98 0.384 -3.016 0.0939
201236 - control-WO -4.267 1.59 81 -9.45 0.918 -2.681 0.2022
201245 - control-WO -3.367 1.59 81 -8.55 1.818 -2.115 0.5292
201823 - control-WO -4.100 1.59 81 -9.28 1.084 -2.576 0.2499
201835 - control-WO -1.500 1.59 81 -6.68 3.684 -0.942 0.9946
201836 - control-WO -3.167 1.59 81 -8.35 2.018 -1.989 0.6152
201851 - control-WO -0.133 1.59 81 -5.32 5.051 -0.084 1.0000
201858 - control-WO 2.967 1.59 81 -2.22 8.151 1.864 0.6985
201859 - control-WO -2.833 1.59 81 -8.02 2.351 -1.780 0.7506
201870 - control-WO -0.533 1.84 81 -6.52 5.453 -0.290 1.0000
201875 - control-WO -2.900 1.59 81 -8.08 2.284 -1.822 0.7250
201876 - control-WO -3.467 1.59 81 -8.65 1.718 -2.178 0.4865
201879 - control-WO -2.367 1.59 81 -7.55 2.818 -1.487 0.8958
201885 - control-WO -0.533 1.59 81 -5.72 4.651 -0.335 1.0000
201887 - control-WO -2.033 2.43 81 -9.95 5.886 -0.836 0.9978
201888 - control-WO -0.933 1.59 81 -6.12 4.251 -0.586 0.9999
201889 - control-WO -4.067 1.59 81 -9.25 1.118 -2.555 0.2602
201895 - control-WO -3.733 2.43 81 -11.65 4.186 -1.535 0.8764
201910 - control-WO -4.467 1.59 81 -9.65 0.718 -2.806 0.1540
201917 - control-WO -0.567 1.59 81 -5.75 4.618 -0.356 1.0000
201933 - control-WO -5.133 2.43 81 -13.05 2.786 -2.111 0.5319
201936 - control-WO -0.333 1.59 81 -5.52 4.851 -0.209 1.0000
control_W - control-WO -1.933 1.30 81 -6.17 2.300 -1.488 0.8955
Confidence level used: 0.95
Conf-level adjustment: dunnettx method for 45 estimates
P value adjustment: dunnettx method for 45 tests 1.1 - CLD - (Pairwise by isolate)
CLD(lsmeans(lm_set_1_DR, ~isolate), alpha=0.1)'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead. isolate lsmean SE df lower.CL upper.CL .group
201933 1.60 2.251 81 -2.8788 6.08 12
201084 1.87 1.300 81 -0.7192 4.45 1
201173 1.93 1.300 81 -0.6525 4.52 1
201910 2.27 1.300 81 -0.3192 4.85 1
201236 2.47 1.300 81 -0.1192 5.05 1
200715 2.50 2.251 81 -1.9788 6.98 12
201823 2.63 1.300 81 0.0475 5.22 12
200659 2.63 1.300 81 0.0475 5.22 12
201889 2.67 1.300 81 0.0808 5.25 12
200488 2.97 1.300 81 0.3808 5.55 12
201895 3.00 2.251 81 -1.4788 7.48 12
201876 3.27 1.300 81 0.6808 5.85 12
201245 3.37 1.300 81 0.7808 5.95 12
200444 3.40 2.251 81 -1.0788 7.88 12
200726 3.53 1.300 81 0.9475 6.12 12
201836 3.57 1.300 81 0.9808 6.15 12
201162 3.57 1.300 81 0.9808 6.15 12
201875 3.83 1.300 81 1.2475 6.42 12
201859 3.90 1.300 81 1.3142 6.49 12
201153 4.20 1.300 81 1.6142 6.79 12
201025 4.23 1.300 81 1.6475 6.82 12
201879 4.37 1.300 81 1.7808 6.95 12
200496 4.53 1.300 81 1.9475 7.12 12
200939 4.63 1.300 81 2.0475 7.22 12
200661 4.70 1.300 81 2.1142 7.29 12
201887 4.70 2.251 81 0.2212 9.18 12
control_W 4.80 0.919 81 2.9715 6.63 12
200725 4.90 1.592 81 1.7330 8.07 12
201085 4.90 2.251 81 0.4212 9.38 12
200902 5.13 1.300 81 2.5475 7.72 12
200808 5.13 1.300 81 2.5475 7.72 12
200556 5.17 1.300 81 2.5808 7.75 12
201835 5.23 1.300 81 2.6475 7.82 12
201888 5.80 1.300 81 3.2142 8.39 12
201045 5.93 1.300 81 3.3475 8.52 12
201917 6.17 1.300 81 3.5808 8.75 12
201885 6.20 1.300 81 3.6142 8.79 12
201870 6.20 1.592 81 3.0330 9.37 12
200270 6.35 1.592 81 3.1830 9.52 12
201936 6.40 1.300 81 3.8142 8.99 12
200727 6.50 1.592 81 3.3330 9.67 12
201851 6.60 1.300 81 4.0142 9.19 12
control-WO 6.73 0.919 81 4.9049 8.56 12
200275 7.93 1.300 81 5.3475 10.52 12
200533 9.45 1.592 81 6.2830 12.62 12
201858 9.70 1.300 81 7.1142 12.29 2
Confidence level used: 0.95
P value adjustment: tukey method for comparing a family of 46 estimates
significance level used: alpha = 0.1 plot(CLD(lsmeans(lm_set_1_DR, ~isolate), alpha=0.1), main="All pairwise comparisons for Mean Dry Root Weight", xlab = "Mean Weight (mg)", ylab = "Isolate ID")'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead.
1.1. - CLD - Mean Estimate Differences to Control_W
Compact Letter Display of estimates for Dry Root Mean weight (mg) differences between plants that were inoculated with a mucilage isolates and the non-inoculated control (Control_W).
plot(CLD(lsmeans(lm_set_1_DR, trt.vs.ctrl~isolate,ref=45)$contrasts, alpha=0.1), main="Mean Dry Root Weight Differences of Inoculated Plants vs. Control_W", xlab = "Difference of Mean Estimates (mg)", ylab = "Contrasts")'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead.No information available to display confidence limits
1.1. - CLD - Mean Estimate Differences to Control-WO
Compact Letter Display of estimates for Dry Root Mean weight (mg) differences between plants that were inoculated with a mucilage isolates and the non-inoculated control (Control-WO).
plot(CLD(lsmeans(lm_set_1_DR, trt.vs.ctrl~isolate,ref=46)$contrasts, alpha=0.1), main="Mean Dry Root Weight Differences of Inoculated Plants vs. Control-WO", xlab = "Difference of Mean Estimates (mg)", ylab = "Contrasts")'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead.No information available to display confidence limits
Based on the means comparison analyses above of the dry root weight measurements for plants of inoculation batch 1, there appears to be one isolate, BCW201858, that has a significant effect on the mean dry root weight. This is the most apparent when comparing to control_W, where the confidence intervals for these mean weight estimates do not overlap [see: 1.1 - CLD - Control_W (Pairwise by isolate)].
1.2. Dry Shoot Weight
# linear model of dry shoot data
lm_set_1_DS <- lm(mg ~ 1 + isolate, set_1_DS)
op = par(mfrow=c(1,2))
plot(lm_set_1_DS, which = c(2,3))not plotting observations with leverage one:
13, 35, 65, 69par(op)
1.2. - Anova
anova(lm_set_1_DS)Analysis of Variance Table
Response: mg
Df Sum Sq Mean Sq F value Pr(>F)
isolate 45 5810.1 129.113 2.5566 0.0001118 ***
Residuals 82 4141.2 50.502
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 11.2. - LSMeans (Control_W) - Dunnett
lsm_s1.dun.DS <- summary(lsmeans(lm_set_1_DS, trt.vs.ctrl ~isolate, ref=45)$contrasts, infer = c(T,T))
write.csv(lsm_s1.dun.DS, "./lsmeans_summary_tables/lsm_s1.dun.DS.csv")1.2. - LSMeans (Control-WO) - Dunnett
summary(lsmeans(lm_set_1_DS, trt.vs.ctrl ~isolate, ref=46)$contrasts, infer = c(T,T)) contrast estimate SE df lower.CL upper.CL t.ratio p.value
200270 - control-WO 9.233 5.80 82 -9.66 28.13 1.591 0.8518
200275 - control-WO 3.900 5.03 82 -12.46 20.26 0.776 0.9988
200444 - control-WO 5.033 7.68 82 -19.96 30.03 0.656 0.9997
200488 - control-WO -1.167 5.03 82 -17.53 15.19 -0.232 1.0000
200496 - control-WO 1.833 5.03 82 -14.53 18.19 0.365 1.0000
200533 - control-WO -4.067 5.80 82 -22.96 14.83 -0.701 0.9995
200556 - control-WO -5.133 5.03 82 -21.49 11.23 -1.022 0.9903
200659 - control-WO 4.333 5.03 82 -12.03 20.69 0.862 0.9972
200661 - control-WO 10.233 5.03 82 -6.13 26.59 2.036 0.5831
200715 - control-WO -8.067 7.68 82 -33.06 16.93 -1.051 0.9882
200725 - control-WO -11.717 5.80 82 -30.61 7.18 -2.019 0.5949
200726 - control-WO 4.733 5.03 82 -11.63 21.09 0.942 0.9946
200727 - control-WO 19.100 5.03 82 2.74 35.46 3.801 0.0099
200808 - control-WO -2.900 5.03 82 -19.26 13.46 -0.577 0.9999
200902 - control-WO 1.967 5.03 82 -14.39 18.33 0.391 1.0000
200939 - control-WO 6.167 5.03 82 -10.19 22.53 1.227 0.9665
201025 - control-WO 8.700 5.03 82 -7.66 25.06 1.731 0.7791
201045 - control-WO 8.000 5.03 82 -8.36 24.36 1.592 0.8515
201084 - control-WO -11.967 5.03 82 -28.33 4.39 -2.381 0.3558
201085 - control-WO 3.733 7.68 82 -21.26 28.73 0.486 1.0000
201153 - control-WO 13.133 5.03 82 -3.23 29.49 2.614 0.2317
201162 - control-WO 2.867 5.03 82 -13.49 19.23 0.570 0.9999
201173 - control-WO -2.767 5.03 82 -19.13 13.59 -0.551 0.9999
201236 - control-WO 4.467 5.03 82 -11.89 20.83 0.889 0.9965
201245 - control-WO 9.367 5.03 82 -6.99 25.73 1.864 0.6984
201823 - control-WO -1.700 5.03 82 -18.06 14.66 -0.338 1.0000
201835 - control-WO 13.500 5.03 82 -2.86 29.86 2.687 0.1995
201836 - control-WO 10.100 5.03 82 -6.26 26.46 2.010 0.6013
201851 - control-WO 8.767 5.03 82 -7.59 25.13 1.745 0.7715
201858 - control-WO -1.967 5.03 82 -18.33 14.39 -0.391 1.0000
201859 - control-WO 8.733 5.03 82 -7.63 25.09 1.738 0.7753
201870 - control-WO -5.517 5.80 82 -24.41 13.38 -0.951 0.9942
201875 - control-WO 9.300 5.03 82 -7.06 25.66 1.851 0.7069
201876 - control-WO 6.633 5.03 82 -9.73 22.99 1.320 0.9473
201879 - control-WO 5.367 5.03 82 -10.99 21.73 1.068 0.9868
201885 - control-WO 8.667 5.03 82 -7.69 25.03 1.725 0.7829
201887 - control-WO 10.933 7.68 82 -14.06 35.93 1.424 0.9179
201888 - control-WO -3.133 5.03 82 -19.49 13.23 -0.624 0.9998
201889 - control-WO 8.467 5.03 82 -7.89 24.83 1.685 0.8048
201895 - control-WO -0.967 7.68 82 -25.96 24.03 -0.126 1.0000
201910 - control-WO -3.333 5.03 82 -19.69 13.03 -0.663 0.9997
201917 - control-WO 7.567 5.03 82 -8.79 23.93 1.506 0.8886
201933 - control-WO -7.967 7.68 82 -32.96 17.03 -1.038 0.9892
201936 - control-WO 14.167 5.03 82 -2.19 30.53 2.819 0.1494
control_W - control-WO -1.983 4.10 82 -15.34 11.38 -0.483 1.0000
Confidence level used: 0.95
Conf-level adjustment: dunnettx method for 45 estimates
P value adjustment: dunnettx method for 45 tests 1.2 - CLD - (Pairwise by isolate)
CLD(lsmeans(lm_set_1_DS, ~isolate), alpha=0.1)'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead. isolate lsmean SE df lower.CL upper.CL .group
201084 5.20 4.10 82 -2.96 13.4 1
200725 5.45 5.03 82 -4.55 15.4 12
200715 9.10 7.11 82 -5.04 23.2 1234
201933 9.20 7.11 82 -4.94 23.3 1234
201870 11.65 5.03 82 1.65 21.6 1234
200556 12.03 4.10 82 3.87 20.2 123
200533 13.10 5.03 82 3.10 23.1 1234
201910 13.83 4.10 82 5.67 22.0 1234
201888 14.03 4.10 82 5.87 22.2 1234
200808 14.27 4.10 82 6.10 22.4 1234
201173 14.40 4.10 82 6.24 22.6 1234
control_W 15.18 2.90 82 9.41 21.0 123
201858 15.20 4.10 82 7.04 23.4 1234
201823 15.47 4.10 82 7.30 23.6 1234
200488 16.00 4.10 82 7.84 24.2 1234
201895 16.20 7.11 82 2.06 30.3 1234
control-WO 17.17 2.90 82 11.40 22.9 1234
200496 19.00 4.10 82 10.84 27.2 1234
200902 19.13 4.10 82 10.97 27.3 1234
201162 20.03 4.10 82 11.87 28.2 1234
201085 20.90 7.11 82 6.76 35.0 1234
200275 21.07 4.10 82 12.90 29.2 1234
200659 21.50 4.10 82 13.34 29.7 1234
201236 21.63 4.10 82 13.47 29.8 1234
200726 21.90 4.10 82 13.74 30.1 1234
200444 22.20 7.11 82 8.06 36.3 1234
201879 22.53 4.10 82 14.37 30.7 1234
200939 23.33 4.10 82 15.17 31.5 1234
201876 23.80 4.10 82 15.64 32.0 1234
201917 24.73 4.10 82 16.57 32.9 1234
201045 25.17 4.10 82 17.00 33.3 1234
201889 25.63 4.10 82 17.47 33.8 1234
201885 25.83 4.10 82 17.67 34.0 1234
201025 25.87 4.10 82 17.70 34.0 1234
201859 25.90 4.10 82 17.74 34.1 1234
201851 25.93 4.10 82 17.77 34.1 1234
200270 26.40 5.03 82 16.40 36.4 1234
201875 26.47 4.10 82 18.30 34.6 1234
201245 26.53 4.10 82 18.37 34.7 1234
201836 27.27 4.10 82 19.10 35.4 1234
200661 27.40 4.10 82 19.24 35.6 1234
201887 28.10 7.11 82 13.96 42.2 1234
201153 30.30 4.10 82 22.14 38.5 234
201835 30.67 4.10 82 22.50 38.8 34
201936 31.33 4.10 82 23.17 39.5 34
200727 36.27 4.10 82 28.10 44.4 4
Confidence level used: 0.95
P value adjustment: tukey method for comparing a family of 46 estimates
significance level used: alpha = 0.1 plot(CLD(lsmeans(lm_set_1_DS, ~isolate), alpha=0.1), main="All pairwise comparisons for Mean Dry Shoot Weight", xlab = "Mean Weight (mg)", ylab = "Isolate ID")'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead.
1.2. - CLD - Mean Estimate Differences to Control_W
Compact Letter Display of estimates for Dry Shoot Mean weight (mg) differences between plants that were inoculated with a mucilage isolates and the non-inoculated control (Control_W).
plot(CLD(lsmeans(lm_set_1_DS, trt.vs.ctrl~isolate,ref=45)$contrasts, alpha=0.1), main="Mean Dry Shoot Weight Differences of Inoculated Plants vs. Control_W", xlab = "Difference of Mean Estimates (mg)", ylab = "Contrasts")'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead.No information available to display confidence limits
1.2. - CLD - Mean Estimate Differences to Control-WO
Compact Letter Display of estimates for Dry Shoot Mean weight (mg) differences between plants that were inoculated with a mucilage isolates and the non-inoculated control (Control-WO).
plot(CLD(lsmeans(lm_set_1_DS, trt.vs.ctrl~isolate,ref=46)$contrasts, alpha=0.1), main="Mean Dry Shoot Weight Differences of Inoculated Plants vs. Control-WO", xlab = "Difference of Mean Estimates (mg)", ylab = "Contrasts")'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead.No information available to display confidence limits
BCW200727 has a mean estimate with non-overlaping Confidence intervals to those of the control_W estimate.
1.3. Wet Root Weight
# linear model of Wet Root data
lm_set_1_WR <- lm(mg ~ 1 + isolate, set_1_WR)
#op = par(mfrow=c(1,2))
plot(lm_set_1_WR, which = c(2,3))not plotting observations with leverage one:
13, 17, 35, 65, 69, 70, 108
#par(op)1.3. - Anova
anova(lm_set_1_WR)Analysis of Variance Table
Response: mg
Df Sum Sq Mean Sq F value Pr(>F)
isolate 46 133403 2900.06 3.146 3.444e-06 ***
Residuals 80 73745 921.82
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1summary(lm_set_1_WR, infer=c(T,T))
Call:
lm(formula = mg ~ 1 + isolate, data = set_1_WR)
Residuals:
Min 1Q Median 3Q Max
-91.333 -11.783 0.167 10.600 77.517
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 91.400 21.469 4.257 5.59e-05 ***
isolate200275 42.500 27.716 1.533 0.12912
isolate200444 -59.200 37.185 -1.592 0.11532
isolate200488 -50.867 27.716 -1.835 0.07018 .
isolate200496 -29.933 27.716 -1.080 0.28339
isolate200533 62.400 30.361 2.055 0.04312 *
isolate200556 65.233 27.716 2.354 0.02105 *
isolate200659 -51.267 27.716 -1.850 0.06805 .
isolate200661 -55.267 27.716 -1.994 0.04956 *
isolate200715 -87.700 37.185 -2.358 0.02079 *
isolate200725 -30.750 30.361 -1.013 0.31421
isolate200726 -50.967 27.716 -1.839 0.06964 .
isolate200727 -51.567 27.716 -1.861 0.06648 .
isolate200808 -40.767 27.716 -1.471 0.14525
isolate200902 4.033 27.716 0.146 0.88466
isolate200939 -28.267 27.716 -1.020 0.31087
isolate201025 -21.267 27.716 -0.767 0.44516
isolate201045 -13.267 27.716 -0.479 0.63348
isolate201084 -70.300 27.716 -2.536 0.01314 *
isolate201085 -19.400 37.185 -0.522 0.60331
isolate201153 -30.000 30.361 -0.988 0.32608
isolate201162 -34.333 27.716 -1.239 0.21906
isolate201173 -70.133 27.716 -2.530 0.01335 *
isolate201236 -48.000 27.716 -1.732 0.08715 .
isolate201245 -74.750 30.361 -2.462 0.01597 *
isolate201823 -73.433 27.716 -2.649 0.00971 **
isolate201835 -21.967 27.716 -0.793 0.43038
isolate201836 -43.167 27.716 -1.557 0.12331
isolate201851 23.600 27.716 0.851 0.39704
isolate201856 -6.500 37.185 -0.175 0.86168
isolate201858 -53.900 27.716 -1.945 0.05532 .
isolate201859 -36.667 27.716 -1.323 0.18962
isolate201870 11.850 30.361 0.390 0.69735
isolate201875 -44.600 27.716 -1.609 0.11152
isolate201876 -36.400 27.716 -1.313 0.19283
isolate201879 -44.200 27.716 -1.595 0.11471
isolate201885 20.100 27.716 0.725 0.47044
isolate201887 -28.000 37.185 -0.753 0.45366
isolate201888 -45.167 27.716 -1.630 0.10711
isolate201889 -34.467 27.716 -1.244 0.21729
isolate201895 -7.400 37.185 -0.199 0.84276
isolate201910 -50.967 27.716 -1.839 0.06964 .
isolate201917 -3.833 27.716 -0.138 0.89035
isolate201933 -76.400 37.185 -2.055 0.04318 *
isolate201936 5.833 27.716 0.210 0.83384
isolatecontrol_W -30.617 24.790 -1.235 0.22043
isolatecontrol-WO -21.717 24.790 -0.876 0.38364
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 30.36 on 80 degrees of freedom
(59 observations deleted due to missingness)
Multiple R-squared: 0.644, Adjusted R-squared: 0.4393
F-statistic: 3.146 on 46 and 80 DF, p-value: 3.444e-061.3. - LSMeans (Control_W)
lsm_s1.dun.WR <- summary(lsmeans(lm_set_1_WR, trt.vs.ctrl ~isolate, ref=46)$contrasts, infer = c(T,T))
write.csv(lsm_s1.dun.WR, "./lsmeans_summary_tables/lsm_s1.dun.WR.csv")1.3. - LSMeans (Control-WO)
summary(lsmeans(lm_set_1_WR, trt.vs.ctrl ~isolate, ref=46)$contrasts, infer = c(T,T)) contrast estimate SE df lower.CL upper.CL t.ratio p.value
200270 - control_W 30.617 24.8 80 -50.31 111.5 1.235 0.9665
200275 - control_W 73.117 21.5 80 3.03 143.2 3.406 0.0337
200444 - control_W -28.583 32.8 80 -135.65 78.5 -0.872 0.9972
200488 - control_W -20.250 21.5 80 -90.34 49.8 -0.943 0.9949
200496 - control_W 0.683 21.5 80 -69.41 70.8 0.032 1.0000
200533 - control_W 93.017 24.8 80 12.09 173.9 3.752 0.0119
200556 - control_W 95.850 21.5 80 25.76 165.9 4.465 0.0011
200659 - control_W -20.650 21.5 80 -90.74 49.4 -0.962 0.9941
200661 - control_W -24.650 21.5 80 -94.74 45.4 -1.148 0.9793
200715 - control_W -57.083 32.8 80 -164.15 50.0 -1.741 0.7782
200725 - control_W -0.133 24.8 80 -81.06 80.8 -0.005 1.0000
200726 - control_W -20.350 21.5 80 -90.44 49.7 -0.948 0.9947
200727 - control_W -20.950 21.5 80 -91.04 49.1 -0.976 0.9934
200808 - control_W -10.150 21.5 80 -80.24 59.9 -0.473 1.0000
200902 - control_W 34.650 21.5 80 -35.44 104.7 1.614 0.8448
200939 - control_W 2.350 21.5 80 -67.74 72.4 0.109 1.0000
201025 - control_W 9.350 21.5 80 -60.74 79.4 0.436 1.0000
201045 - control_W 17.350 21.5 80 -52.74 87.4 0.808 0.9984
201084 - control_W -39.683 21.5 80 -109.77 30.4 -1.848 0.7132
201085 - control_W 11.217 32.8 80 -95.85 118.3 0.342 1.0000
201153 - control_W 0.617 24.8 80 -80.31 81.5 0.025 1.0000
201162 - control_W -3.717 21.5 80 -73.81 66.4 -0.173 1.0000
201173 - control_W -39.517 21.5 80 -109.61 30.6 -1.841 0.7181
201236 - control_W -17.383 21.5 80 -87.47 52.7 -0.810 0.9984
201245 - control_W -44.133 24.8 80 -125.06 36.8 -1.780 0.7550
201823 - control_W -42.817 21.5 80 -112.91 27.3 -1.994 0.6170
201835 - control_W 8.650 21.5 80 -61.44 78.7 0.403 1.0000
201836 - control_W -12.550 21.5 80 -82.64 57.5 -0.585 0.9999
201851 - control_W 54.217 21.5 80 -15.87 124.3 2.525 0.2792
201856 - control_W 24.117 32.8 80 -82.95 131.2 0.735 0.9993
201858 - control_W -23.283 21.5 80 -93.37 46.8 -1.085 0.9860
201859 - control_W -6.050 21.5 80 -76.14 64.0 -0.282 1.0000
201870 - control_W 42.467 24.8 80 -38.46 123.4 1.713 0.7937
201875 - control_W -13.983 21.5 80 -84.07 56.1 -0.651 0.9997
201876 - control_W -5.783 21.5 80 -75.87 64.3 -0.269 1.0000
201879 - control_W -13.583 21.5 80 -83.67 56.5 -0.633 0.9998
201885 - control_W 50.717 21.5 80 -19.37 120.8 2.362 0.3720
201887 - control_W 2.617 32.8 80 -104.45 109.7 0.080 1.0000
201888 - control_W -14.550 21.5 80 -84.64 55.5 -0.678 0.9996
201889 - control_W -3.850 21.5 80 -73.94 66.2 -0.179 1.0000
201895 - control_W 23.217 32.8 80 -83.85 130.3 0.708 0.9995
201910 - control_W -20.350 21.5 80 -90.44 49.7 -0.948 0.9947
201917 - control_W 26.783 21.5 80 -43.31 96.9 1.248 0.9643
201933 - control_W -45.783 32.8 80 -152.85 61.3 -1.396 0.9291
201936 - control_W 36.450 21.5 80 -33.64 106.5 1.698 0.8020
control-WO - control_W 8.900 17.5 80 -48.33 66.1 0.508 1.0000
Confidence level used: 0.95
Conf-level adjustment: dunnettx method for 46 estimates
P value adjustment: dunnettx method for 46 tests View(set_1_DR)1.3 - CLD - (Pairwise by isolate)
CLD(lsmeans(lm_set_1_WR, ~isolate), alpha=0.1)'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead. isolate lsmean SE df lower.CL upper.CL .group
200715 3.7 30.4 80 -56.72 64.1 123
201933 15.0 30.4 80 -45.42 75.4 1234
201245 16.6 21.5 80 -26.07 59.4 12
201823 18.0 17.5 80 -16.92 52.9 1
201084 21.1 17.5 80 -13.78 56.0 12
201173 21.3 17.5 80 -13.62 56.2 12
200444 32.2 30.4 80 -28.22 92.6 12345
200661 36.1 17.5 80 1.25 71.0 12
201858 37.5 17.5 80 2.62 72.4 12
200727 39.8 17.5 80 4.95 74.7 123
200659 40.1 17.5 80 5.25 75.0 123
201910 40.4 17.5 80 5.55 75.3 123
200726 40.4 17.5 80 5.55 75.3 123
200488 40.5 17.5 80 5.65 75.4 123
201236 43.4 17.5 80 8.52 78.3 123
201888 46.2 17.5 80 11.35 81.1 1234
201875 46.8 17.5 80 11.92 81.7 1234
201879 47.2 17.5 80 12.32 82.1 1234
201836 48.2 17.5 80 13.35 83.1 1234
200808 50.6 17.5 80 15.75 85.5 1234
201859 54.7 17.5 80 19.85 89.6 1234
201876 55.0 17.5 80 20.12 89.9 1234
201889 56.9 17.5 80 22.05 91.8 1234
201162 57.1 17.5 80 22.18 92.0 1234
200725 60.6 21.5 80 17.93 103.4 12345
control_W 60.8 12.4 80 36.12 85.5 1234
201153 61.4 21.5 80 18.68 104.1 12345
200496 61.5 17.5 80 26.58 96.4 12345
200939 63.1 17.5 80 28.25 98.0 12345
201887 63.4 30.4 80 2.98 123.8 12345
201835 69.4 17.5 80 34.55 104.3 12345
control-WO 69.7 12.4 80 45.02 94.4 1234
201025 70.1 17.5 80 35.25 105.0 12345
201085 72.0 30.4 80 11.58 132.4 12345
201045 78.1 17.5 80 43.25 113.0 12345
201895 84.0 30.4 80 23.58 144.4 12345
201856 84.9 30.4 80 24.48 145.3 12345
201917 87.6 17.5 80 52.68 122.5 12345
200270 91.4 21.5 80 48.68 134.1 12345
200902 95.4 17.5 80 60.55 130.3 12345
201936 97.2 17.5 80 62.35 132.1 12345
201870 103.2 21.5 80 60.53 146.0 12345
201885 111.5 17.5 80 76.62 146.4 12345
201851 115.0 17.5 80 80.12 149.9 2345
200275 133.9 17.5 80 99.02 168.8 345
200533 153.8 21.5 80 111.08 196.5 45
200556 156.6 17.5 80 121.75 191.5 5
Confidence level used: 0.95
P value adjustment: tukey method for comparing a family of 47 estimates
significance level used: alpha = 0.1 plot(CLD(lsmeans(lm_set_1_WR, ~isolate), alpha=0.1), main="All pairwise comparisons for Mean Wet Root Weight", xlab = "Mean Weight (mg)", ylab = "Isolate ID")'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead.
1.3. - CLD - Mean Estimate Differences to Control_W
Compact Letter Display of estimates for Wet Root Mean weight (mg) differences between plants that were inoculated with a mucilage isolates and the non-inoculated control (Control_W).
plot(CLD(lsmeans(lm_set_1_WR, trt.vs.ctrl~isolate,ref=45)$contrasts, alpha=0.1), main="Mean Wet Root Weight Differences of Inoculated Plants vs. Control_W", xlab = "Difference of Mean Estimates (mg)", ylab = "Contrasts")'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead.No information available to display confidence limits
1.3. - CLD - Mean Estimate Differences to Control-WO
Compact Letter Display of estimates for Wet Root Mean weight (mg) differences between plants that were inoculated with a mucilage isolates and the non-inoculated control (Control-WO).
plot(CLD(lsmeans(lm_set_1_WR, trt.vs.ctrl~isolate,ref=46)$contrasts, alpha=0.1), main="Mean Wet Root Weight Differences of Inoculated Plants vs. Control-WO", xlab = "Difference of Mean Estimates (mg)", ylab = "Contrasts")'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead.No information available to display confidence limits
Regarding wet root mass in milligrams, isolate BCW200556 does not exhibit a mean estimate with a CI that overlaps with those of mean estimates from both controls. This implies the effect is significant at the alpha = 0.1 level.
1.4. Wet Shoot Weight
# linear model of Wet shoot data
lm_set_1_WS <- lm(mg ~ 1 + isolate, set_1_WS)
op = par(mfrow=c(1,2))
plot(lm_set_1_WS, which = c(2,3))not plotting observations with leverage one:
13, 35, 65, 69, 70, 109par(op)
1.4. - Anova
anova(lm_set_1_WS)Analysis of Variance Table
Response: mg
Df Sum Sq Mean Sq F value Pr(>F)
isolate 46 1496704 32537 2.3648 0.0003359 ***
Residuals 82 1128210 13759
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1summary(lm_set_1_WS)
Call:
lm(formula = mg ~ 1 + isolate, data = set_1_WS)
Residuals:
Min 1Q Median 3Q Max
-230.467 -55.800 -0.433 58.433 248.433
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 470.40 82.94 5.671 2.06e-07 ***
isolate200275 -128.63 107.08 -1.201 0.23309
isolate200444 -130.10 143.66 -0.906 0.36779
isolate200488 -251.00 107.08 -2.344 0.02149 *
isolate200496 -197.53 107.08 -1.845 0.06868 .
isolate200533 -299.55 117.30 -2.554 0.01251 *
isolate200556 -128.10 107.08 -1.196 0.23501
isolate200659 -143.63 107.08 -1.341 0.18349
isolate200661 -85.87 107.08 -0.802 0.42492
isolate200715 -322.00 143.66 -2.241 0.02770 *
isolate200725 -333.25 117.30 -2.841 0.00567 **
isolate200726 -126.07 107.08 -1.177 0.24246
isolate200727 60.33 107.08 0.563 0.57466
isolate200808 -231.53 107.08 -2.162 0.03351 *
isolate200902 -174.83 107.08 -1.633 0.10635
isolate200939 -75.33 107.08 -0.704 0.48371
isolate201025 -41.77 107.08 -0.390 0.69750
isolate201045 -91.67 107.08 -0.856 0.39445
isolate201084 -448.90 107.08 -4.192 6.93e-05 ***
isolate201085 -232.10 143.66 -1.616 0.11002
isolate201153 -9.90 107.08 -0.092 0.92656
isolate201162 -137.10 107.08 -1.280 0.20402
isolate201173 -248.27 107.08 -2.319 0.02291 *
isolate201236 -116.67 107.08 -1.090 0.27910
isolate201245 -43.47 107.08 -0.406 0.68585
isolate201823 -249.97 107.08 -2.334 0.02202 *
isolate201835 110.63 107.08 1.033 0.30454
isolate201836 11.47 107.08 0.107 0.91498
isolate201851 -167.20 107.08 -1.561 0.12226
isolate201856 -445.70 143.66 -3.102 0.00263 **
isolate201858 -131.53 107.08 -1.228 0.22281
isolate201859 -81.20 107.08 -0.758 0.45043
isolate201870 -279.90 117.30 -2.386 0.01932 *
isolate201875 -61.40 107.08 -0.573 0.56793
isolate201876 -59.40 107.08 -0.555 0.58058
isolate201879 -76.13 107.08 -0.711 0.47909
isolate201885 -77.53 107.08 -0.724 0.47107
isolate201887 -18.30 143.66 -0.127 0.89895
isolate201888 -233.23 107.08 -2.178 0.03226 *
isolate201889 -111.00 107.08 -1.037 0.30295
isolate201895 -191.10 143.66 -1.330 0.18713
isolate201910 -222.53 107.08 -2.078 0.04081 *
isolate201917 -131.10 107.08 -1.224 0.22433
isolate201933 -329.20 143.66 -2.292 0.02450 *
isolate201936 -74.83 107.08 -0.699 0.48661
isolatecontrol_W -186.88 95.77 -1.951 0.05443 .
isolatecontrol-WO -166.73 95.77 -1.741 0.08545 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 117.3 on 82 degrees of freedom
(57 observations deleted due to missingness)
Multiple R-squared: 0.5702, Adjusted R-squared: 0.3291
F-statistic: 2.365 on 46 and 82 DF, p-value: 0.00033591.4. - LSMeans (Control_W)
lsm_s1.dun.WS <- summary(lsmeans(lm_set_1_WS, trt.vs.ctrl ~isolate, ref=46)$contrasts, infer = c(T,T))
write.csv(lsm_s1.dun.WS, "./lsmeans_summary_tables/lsm_s1.dun.WS.csv")1.4. - LSMeans (Control-WO)
summary(lsmeans(lm_set_1_WS, trt.vs.ctrl ~isolate, ref=47)$contrasts, infer = c(T,T)) contrast estimate SE df lower.CL upper.CL t.ratio p.value
200270 - control-WO 166.733 95.8 82 -145.72 479.2 1.741 0.7781
200275 - control-WO 38.100 82.9 82 -232.49 308.7 0.459 1.0000
200444 - control-WO 36.633 126.7 82 -376.70 450.0 0.289 1.0000
200488 - control-WO -84.267 82.9 82 -354.86 186.3 -1.016 0.9912
200496 - control-WO -30.800 82.9 82 -301.39 239.8 -0.371 1.0000
200533 - control-WO -132.817 95.8 82 -445.27 179.6 -1.387 0.9319
200556 - control-WO 38.633 82.9 82 -231.96 309.2 0.466 1.0000
200659 - control-WO 23.100 82.9 82 -247.49 293.7 0.279 1.0000
200661 - control-WO 80.867 82.9 82 -189.72 351.5 0.975 0.9935
200715 - control-WO -155.267 126.7 82 -568.60 258.1 -1.226 0.9682
200725 - control-WO -166.517 95.8 82 -478.97 145.9 -1.739 0.7794
200726 - control-WO 40.667 82.9 82 -229.92 311.3 0.490 1.0000
200727 - control-WO 227.067 82.9 82 -43.52 497.7 2.738 0.1816
200808 - control-WO -64.800 82.9 82 -335.39 205.8 -0.781 0.9988
200902 - control-WO -8.100 82.9 82 -278.69 262.5 -0.098 1.0000
200939 - control-WO 91.400 82.9 82 -179.19 362.0 1.102 0.9844
201025 - control-WO 124.967 82.9 82 -145.62 395.6 1.507 0.8914
201045 - control-WO 75.067 82.9 82 -195.52 345.7 0.905 0.9963
201084 - control-WO -282.167 82.9 82 -552.76 -11.6 -3.402 0.0338
201085 - control-WO -65.367 126.7 82 -478.70 348.0 -0.516 1.0000
201153 - control-WO 156.833 82.9 82 -113.76 427.4 1.891 0.6860
201162 - control-WO 29.633 82.9 82 -240.96 300.2 0.357 1.0000
201173 - control-WO -81.533 82.9 82 -352.12 189.1 -0.983 0.9931
201236 - control-WO 50.067 82.9 82 -220.52 320.7 0.604 0.9999
201245 - control-WO 123.267 82.9 82 -147.32 393.9 1.486 0.8992
201823 - control-WO -83.233 82.9 82 -353.82 187.4 -1.004 0.9920
201835 - control-WO 277.367 82.9 82 6.78 548.0 3.344 0.0399
201836 - control-WO 178.200 82.9 82 -92.39 448.8 2.148 0.5114
201851 - control-WO -0.467 82.9 82 -271.06 270.1 -0.006 1.0000
201856 - control-WO -278.967 126.7 82 -692.30 134.4 -2.202 0.4753
201858 - control-WO 35.200 82.9 82 -235.39 305.8 0.424 1.0000
201859 - control-WO 85.533 82.9 82 -185.06 356.1 1.031 0.9902
201870 - control-WO -113.167 95.8 82 -425.62 199.3 -1.182 0.9750
201875 - control-WO 105.333 82.9 82 -165.26 375.9 1.270 0.9601
201876 - control-WO 107.333 82.9 82 -163.26 377.9 1.294 0.9551
201879 - control-WO 90.600 82.9 82 -179.99 361.2 1.092 0.9853
201885 - control-WO 89.200 82.9 82 -181.39 359.8 1.075 0.9868
201887 - control-WO 148.433 126.7 82 -264.90 561.8 1.172 0.9764
201888 - control-WO -66.500 82.9 82 -337.09 204.1 -0.802 0.9985
201889 - control-WO 55.733 82.9 82 -214.86 326.3 0.672 0.9996
201895 - control-WO -24.367 126.7 82 -437.70 389.0 -0.192 1.0000
201910 - control-WO -55.800 82.9 82 -326.39 214.8 -0.673 0.9996
201917 - control-WO 35.633 82.9 82 -234.96 306.2 0.430 1.0000
201933 - control-WO -162.467 126.7 82 -575.80 250.9 -1.282 0.9576
201936 - control-WO 91.900 82.9 82 -178.69 362.5 1.108 0.9838
control_W - control-WO -20.150 67.7 82 -241.09 200.8 -0.298 1.0000
Confidence level used: 0.95
Conf-level adjustment: dunnettx method for 46 estimates
P value adjustment: dunnettx method for 46 tests 1.4 - CLD - (Pairwise by isolate)
CLD(lsmeans(lm_set_1_WS, ~isolate), alpha=0.1)'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead. isolate lsmean SE df lower.CL upper.CL .group
201084 21.5 67.7 82 -113.22 156 1
201856 24.7 117.3 82 -208.64 258 12
200725 137.2 82.9 82 -27.85 302 12
201933 141.2 117.3 82 -92.14 375 123
200715 148.4 117.3 82 -84.94 382 123
200533 170.8 82.9 82 5.85 336 123
201870 190.5 82.9 82 25.50 355 123
200488 219.4 67.7 82 84.68 354 123
201823 220.4 67.7 82 85.71 355 123
201173 222.1 67.7 82 87.41 357 123
201888 237.2 67.7 82 102.45 372 123
201085 238.3 117.3 82 4.96 472 123
200808 238.9 67.7 82 104.15 374 123
201910 247.9 67.7 82 113.15 383 123
200496 272.9 67.7 82 138.15 408 123
201895 279.3 117.3 82 45.96 513 123
control_W 283.5 47.9 82 188.26 379 123
200902 295.6 67.7 82 160.85 430 123
201851 303.2 67.7 82 168.48 438 123
control-WO 303.7 47.9 82 208.41 399 123
200659 326.8 67.7 82 192.05 461 123
201162 333.3 67.7 82 198.58 468 123
201858 338.9 67.7 82 204.15 474 123
201917 339.3 67.7 82 204.58 474 123
200444 340.3 117.3 82 106.96 574 123
200275 341.8 67.7 82 207.05 476 123
200556 342.3 67.7 82 207.58 477 123
200726 344.3 67.7 82 209.61 479 123
201236 353.7 67.7 82 219.01 488 123
201889 359.4 67.7 82 224.68 494 123
201045 378.7 67.7 82 244.01 513 123
200661 384.5 67.7 82 249.81 519 123
201859 389.2 67.7 82 254.48 524 123
201885 392.9 67.7 82 258.15 528 123
201879 394.3 67.7 82 259.55 529 23
200939 395.1 67.7 82 260.35 530 23
201936 395.6 67.7 82 260.85 530 23
201875 409.0 67.7 82 274.28 544 23
201876 411.0 67.7 82 276.28 546 23
201245 426.9 67.7 82 292.21 562 23
201025 428.6 67.7 82 293.91 563 23
201887 452.1 117.3 82 218.76 685 123
201153 460.5 67.7 82 325.78 595 23
200270 470.4 82.9 82 305.40 635 23
201836 481.9 67.7 82 347.15 617 23
200727 530.7 67.7 82 396.01 665 23
201835 581.0 67.7 82 446.31 716 3
Confidence level used: 0.95
P value adjustment: tukey method for comparing a family of 47 estimates
significance level used: alpha = 0.1 plot(CLD(lsmeans(lm_set_1_WS, ~isolate), alpha=0.1), main="All pairwise comparisons for Mean Wet Shoot Weight", xlab = "Mean Weight (mg)", ylab = "Isolate ID")'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead.
1.4. - CLD - Mean Estimate Differences to Control_W
Compact Letter Display of estimates for Wet Shoot Mean weight (mg) differences between plants that were inoculated with a mucilage isolates and the non-inoculated control (Control_W).
plot(CLD(lsmeans(lm_set_1_WS, trt.vs.ctrl~isolate,ref=45)$contrasts, alpha=0.1), main="Mean Wet Shoot Weight Differences of Inoculated Plants vs. Control_W", xlab = "Difference of Mean Estimates (mg)", ylab = "Contrasts")'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead.No information available to display confidence limits
1.4. - CLD - Mean Estimate Differences to Control-WO
Compact Letter Display of estimates for Wet Shoot Mean weight (mg) differences between plants that were inoculated with a mucilage isolates and the non-inoculated control (Control-WO).
plot(CLD(lsmeans(lm_set_1_WS, trt.vs.ctrl~isolate,ref=46)$contrasts, alpha=0.1), main="Mean Wet Shoot Weight Differences of Inoculated Plants vs. Control-WO", xlab = "Difference of Mean Estimates (mg)", ylab = "Contrasts")'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead.No information available to display confidence limits
None of the mean estimates for inoculated isolates have non-overlapping confidence intervals with those of non-inoculated controls.
Batch 2.1
# analyze set 2.1 data
str(set_2.1)'data.frame': 600 obs. of 4 variables:
$ isolate: chr "200498" "200498" "200498" "200498" ...
$ rep : int 1 1 1 1 1 1 2 2 2 2 ...
$ sample : chr "WS" "WR" "DS" "DR" ...
$ mg : num 78.5 32.5 7.8 2.7 47512.1 ...#subset by sample type
set_2.1_DR <- filter(set_2.1, sample == "DR")
set_2.1_DS <- filter(set_2.1, sample == "DS")
set_2.1_WR <- filter(set_2.1, sample == "WR")
set_2.1_WS <- filter(set_2.1, sample == "WS")Experimental Design Table
| Structure | Factor | Type | # levels |
|---|---|---|---|
| Treatment | Isolate | Qualitative | 33 |
| Design | MS Media Slants | Experimental Unit | 99 |
| Response | Plant Tissue Weight | Observational Unit | 396 |
| Response | Dry Root Weight (DR) | Variable | 99 |
| Response | Dry Shoot Weight (DS) | Variable | 99 |
| Response | Wet Root Weight (WR) | Variable | 99 |
| Response | Wet Shoot Weight (WS) | Variable | 99 |
- Four sets of inoculations were setup independently, each with independent controls.
- Each set of isolates was treated as an independent experiment for the analysis.
2.1.1. Dry Root Weight
# linear model of dry root data
lm_set_2.1_DR <- lm(mg ~ 1 + isolate, set_2.1_DR)
op = par(mfrow=c(1,2))
plot(lm_set_2.1_DR, which = c(2,3))
par(op)
2.1.1. - Anova
# assess variance - but the data isn't normal
anova(lm_set_2.1_DR)Analysis of Variance Table
Response: mg
Df Sum Sq Mean Sq F value Pr(>F)
isolate 30 233.93 7.7978 1.84 0.02169 *
Residuals 62 262.75 4.2379
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 12.1.1. - LSMeans - Dunnett
lsm_s2.1.dun.DR <- summary(lsmeans(lm_set_2.1_DR, trt.vs.ctrl ~isolate, ref=31)$contrasts, infer = c(T,T))
write.csv(lsm_s2.1.dun.DR, "./lsmeans_summary_tables/lsm_s2.1.dun.DR.csv")2.1.1. - CLD - (Pairwise by isolate)
CLD(lsmeans(lm_set_2.1_DR, ~isolate), alpha=0.1)'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead. isolate lsmean SE df lower.CL upper.CL .group
200723 1.77 1.19 62 -0.609 4.14 1
201894 2.10 1.19 62 -0.276 4.48 12
201303 2.13 1.19 62 -0.243 4.51 12
200498 2.63 1.19 62 0.254 5.01 123
200596 2.73 1.19 62 0.357 5.11 123
200662 3.13 1.19 62 0.757 5.51 123
201187 3.37 1.19 62 0.991 5.74 123
201882 3.40 1.46 62 0.490 6.31 123
control 3.50 1.03 62 1.442 5.56 123
200328 3.53 1.19 62 1.157 5.91 123
201090 3.73 1.19 62 1.357 6.11 123
201883 3.80 1.19 62 1.424 6.18 123
201862 3.87 1.19 62 1.491 6.24 123
200279 3.90 1.19 62 1.524 6.28 123
200443 4.03 1.19 62 1.657 6.41 123
201866 4.13 1.19 62 1.757 6.51 123
200955 4.20 1.19 62 1.824 6.58 123
201884 4.20 1.19 62 1.824 6.58 123
200566 4.33 1.19 62 1.957 6.71 123
201878 4.33 1.19 62 1.957 6.71 123
200505 4.37 1.19 62 1.991 6.74 123
201886 4.43 1.19 62 2.057 6.81 123
200621 5.13 1.19 62 2.757 7.51 123
200810 5.50 1.19 62 3.124 7.88 123
201867 5.50 1.19 62 3.124 7.88 123
200926 5.73 1.19 62 3.357 8.11 123
201809 5.80 1.19 62 3.424 8.18 123
201831 6.17 1.19 62 3.791 8.54 123
200539 7.30 1.19 62 4.924 9.68 123
201864 8.07 1.19 62 5.691 10.44 23
201873 8.43 1.19 62 6.057 10.81 3
Confidence level used: 0.95
P value adjustment: tukey method for comparing a family of 31 estimates
significance level used: alpha = 0.1 plot(CLD(lsmeans(lm_set_2.1_DR, ~isolate), alpha=0.1), main="All pairwise comparisons for Mean Dry Root Weight", xlab = "Mean Weight (mg)", ylab = "Isolate ID")'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead.
2.1.1. - CLD - Mean Estimate Differences to Control
Compact Letter Display of estimates for Dry Root Mean weight (mg) differences between plants that were inoculated with a mucilage isolates and the non-inoculated control.
plot(CLD(lsmeans(lm_set_2.1_DR, trt.vs.ctrl~isolate,ref=31)$contrasts, alpha=0.1), main="Mean Dry Root Weight Differences of Inoculated Plants vs. Control", xlab = "Difference of Mean Estimates (mg)", ylab = "Contrasts")'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead.No information available to display confidence limits
Dry root data suggests that isolate BCW201884 significantly increases root weight of the potato plantlets. I am suspecious of this result and need to check the manually recorded datasheets. The data entered for this isolate could have received a typo during data entry (likely based on the triplicate set entered: 3.6, 87.3, 1.7).
2.1.2. Dry Shoot Weight
# linear model of dry shoot data
lm_set_2.1_DS <- lm(mg ~ 1 + isolate, set_2.1_DS)
op = par(mfrow=c(1,2))
plot(lm_set_2.1_DS, which = c(2,3))
par(op)
2.1.2. - Anova
# assess variance
anova(lm_set_2.1_DS)Analysis of Variance Table
Response: mg
Df Sum Sq Mean Sq F value Pr(>F)
isolate 30 2494.8 83.159 1.686 0.0418 *
Residuals 62 3058.1 49.324
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 12.1.2. - LSMeans - Dunnett
lsm_s2.1.dun.DS <- summary(lsmeans(lm_set_2.1_DS, trt.vs.ctrl ~isolate, ref=31)$contrasts, infer = c(T,T))
write.csv(lsm_s2.1.dun.DS, "./lsmeans_summary_tables/lsm_s2.1.dun.DS.csv")2.1.2 - CLD - (Pairwise by isolate)
CLD(lsmeans(lm_set_2.1_DS, ~isolate), alpha=0.1)'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead. isolate lsmean SE df lower.CL upper.CL .group
200498 6.97 4.05 62 -1.14 15.1 1
control 9.75 3.51 62 2.73 16.8 1
200723 10.93 4.05 62 2.83 19.0 12
201883 11.30 4.05 62 3.19 19.4 12
200596 12.93 4.05 62 4.83 21.0 12
201090 13.13 4.05 62 5.03 21.2 12
200926 13.53 4.05 62 5.43 21.6 12
201882 13.60 4.97 62 3.67 23.5 12
201894 14.03 4.05 62 5.93 22.1 12
200279 14.93 4.05 62 6.83 23.0 12
201303 15.20 4.05 62 7.09 23.3 12
201866 15.57 4.05 62 7.46 23.7 12
201862 15.63 4.05 62 7.53 23.7 12
201878 15.77 4.05 62 7.66 23.9 12
200505 15.90 4.05 62 7.79 24.0 12
201187 16.27 4.05 62 8.16 24.4 12
200328 16.43 4.05 62 8.33 24.5 12
201886 17.70 4.05 62 9.59 25.8 12
200443 18.23 4.05 62 10.13 26.3 12
200621 19.43 4.05 62 11.33 27.5 12
200662 20.67 4.05 62 12.56 28.8 12
200566 20.77 4.05 62 12.66 28.9 12
201809 21.13 4.05 62 13.03 29.2 12
201831 21.37 4.05 62 13.26 29.5 12
201867 21.90 4.05 62 13.79 30.0 12
201884 22.33 4.05 62 14.23 30.4 12
200539 22.50 4.05 62 14.39 30.6 12
200955 22.63 4.05 62 14.53 30.7 12
201864 24.60 4.05 62 16.49 32.7 12
201873 25.27 4.05 62 17.16 33.4 12
200810 31.50 4.05 62 23.39 39.6 2
Confidence level used: 0.95
P value adjustment: tukey method for comparing a family of 31 estimates
significance level used: alpha = 0.1 plot(CLD(lsmeans(lm_set_2.1_DS, ~isolate), alpha=0.1), main="All pairwise comparisons for Mean Dry Shoot Weight", xlab = "Mean Weight (mg)", ylab = "Isolate ID")'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead.
2.1.2. - CLD - Mean Estimate Differences to Control
Compact Letter Display of estimates for Dry Shoot Mean weight (mg) differences between plants that were inoculated with a mucilage isolates and the non-inoculated control.
plot(CLD(lsmeans(lm_set_2.1_DS, trt.vs.ctrl~isolate,ref=31)$contrasts, alpha=0.1), main="Mean Dry Shoot Weight Differences of Inoculated Plants vs. Control", xlab = "Difference of Mean Estimates (mg)", ylab = "Contrasts")'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead.No information available to display confidence limits
The difference between the control and BCW200810 is statistically significant at an alpha level of 0.1.
2.1.3. Wet Root Weight
# linear model of wet root data
lm_set_2.1_WR <- lm(mg ~ 1 + isolate, set_2.1_WR)
op = par(mfrow=c(1,2))
plot(lm_set_2.1_WR, which = c(2,3))
par(op)
2.1.3. - Anova
#assess variance
anova(lm_set_2.1_WR)Analysis of Variance Table
Response: mg
Df Sum Sq Mean Sq F value Pr(>F)
isolate 30 44211 1473.71 1.9916 0.0112 *
Residuals 62 45877 739.96
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 12.1.3. - LSMeans - Dunnett
lsm_s2.1.dun.WR <- summary(lsmeans(lm_set_2.1_WR, trt.vs.ctrl ~isolate, ref=31)$contrasts, infer = c(T,T))
write.csv(lsm_s2.1.dun.WR, "./lsmeans_summary_tables/lsm_s2.1.dun.WR.csv")2.1.3. - CLD - (Pairwise by isolate)
CLD(lsmeans(lm_set_2.1_WR, ~isolate), alpha=0.1)'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead. isolate lsmean SE df lower.CL upper.CL .group
200723 24.7 15.7 62 -6.73 56.1 1
201303 33.0 15.7 62 1.57 64.4 12
200498 36.5 15.7 62 5.11 67.9 12
control 37.5 13.6 62 10.31 64.7 12
200662 37.8 15.7 62 6.44 69.2 12
200443 38.9 15.7 62 7.47 70.3 12
201894 39.0 15.7 62 7.61 70.4 12
200596 40.9 15.7 62 9.47 72.3 12
201187 42.4 15.7 62 11.01 73.8 12
201883 44.3 15.7 62 12.91 75.7 12
201878 44.6 15.7 62 13.17 76.0 12
200328 45.8 15.7 62 14.44 77.2 12
200566 47.5 15.7 62 16.11 78.9 12
200505 48.6 15.7 62 17.24 80.0 12
201884 49.5 15.7 62 18.14 80.9 12
201862 49.9 15.7 62 18.51 81.3 12
201090 50.3 15.7 62 18.91 81.7 12
201882 50.8 19.2 62 12.35 89.2 12
201886 51.1 15.7 62 19.71 82.5 12
200621 51.8 15.7 62 20.44 83.2 12
201866 52.3 15.7 62 20.87 83.7 12
200279 53.5 15.7 62 22.11 84.9 12
200955 55.7 15.7 62 24.34 87.1 12
200926 72.5 15.7 62 41.07 103.9 12
201867 75.8 15.7 62 44.37 107.2 12
200539 79.4 15.7 62 47.97 110.8 12
201831 79.9 15.7 62 48.47 111.3 12
200810 98.4 15.7 62 67.01 129.8 12
201809 99.6 15.7 62 68.17 131.0 12
201864 102.9 15.7 62 71.54 134.3 12
201873 107.9 15.7 62 76.54 139.3 2
Confidence level used: 0.95
P value adjustment: tukey method for comparing a family of 31 estimates
significance level used: alpha = 0.1 plot(CLD(lsmeans(lm_set_2.1_WR, ~isolate), alpha=0.1), main="All pairwise comparisons for Mean Wet Root Weight", xlab = "Mean Weight (mg)", ylab = "Isolate ID")'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead.
2.1.3. - CLD - Mean Estimate Differences to Control
Compact Letter Display of estimates for Wet Root Mean weight (mg) differences between plants that were inoculated with a mucilage isolates and the non-inoculated control.
plot(CLD(lsmeans(lm_set_2.1_WR, trt.vs.ctrl~isolate,ref=31)$contrasts, alpha=0.1), main="Mean Wet Root Weight Differences of Inoculated Plants vs. Control", xlab = "Difference of Mean Estimates (mg)", ylab = "Contrasts")'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead.No information available to display confidence limits
Based on the modeling and the plot of mean wet root weights, the cluster of four isolates with mean wet root differences around 60 mg appear to be strong candidates. Isolates BCW201873, BCW200810, BCW201809, BCW201864.
2.1.4. Wet Shoot Weight
# linear model of wet shoot data
lm_set_2.1_WS <- lm(mg ~ 1 + isolate, set_2.1_WS)
op = par(mfrow=c(1,2))
plot(lm_set_2.1_WS, which = c(2,3))
par(op)
2.1.4. - Anova
# assess variance
anova(lm_set_2.1_WS)Analysis of Variance Table
Response: mg
Df Sum Sq Mean Sq F value Pr(>F)
isolate 30 604682 20156 1.9486 0.01353 *
Residuals 62 641310 10344
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 12.1.4. - LSMeans - Dunnett
lsm_s2.1.dun.WS <- summary(lsmeans(lm_set_2.1_WS, trt.vs.ctrl ~isolate, ref=31)$contrasts, infer = c(T,T))
write.csv(lsm_s2.1.dun.WS, "./lsmeans_summary_tables/lsm_s2.1.dun.WS.csv")2.1.4. - CLD - (Pairwise by isolate)
CLD(lsmeans(lm_set_2.1_DS, ~isolate), alpha=0.1)'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead. isolate lsmean SE df lower.CL upper.CL .group
200498 6.97 4.05 62 -1.14 15.1 1
control 9.75 3.51 62 2.73 16.8 1
200723 10.93 4.05 62 2.83 19.0 12
201883 11.30 4.05 62 3.19 19.4 12
200596 12.93 4.05 62 4.83 21.0 12
201090 13.13 4.05 62 5.03 21.2 12
200926 13.53 4.05 62 5.43 21.6 12
201882 13.60 4.97 62 3.67 23.5 12
201894 14.03 4.05 62 5.93 22.1 12
200279 14.93 4.05 62 6.83 23.0 12
201303 15.20 4.05 62 7.09 23.3 12
201866 15.57 4.05 62 7.46 23.7 12
201862 15.63 4.05 62 7.53 23.7 12
201878 15.77 4.05 62 7.66 23.9 12
200505 15.90 4.05 62 7.79 24.0 12
201187 16.27 4.05 62 8.16 24.4 12
200328 16.43 4.05 62 8.33 24.5 12
201886 17.70 4.05 62 9.59 25.8 12
200443 18.23 4.05 62 10.13 26.3 12
200621 19.43 4.05 62 11.33 27.5 12
200662 20.67 4.05 62 12.56 28.8 12
200566 20.77 4.05 62 12.66 28.9 12
201809 21.13 4.05 62 13.03 29.2 12
201831 21.37 4.05 62 13.26 29.5 12
201867 21.90 4.05 62 13.79 30.0 12
201884 22.33 4.05 62 14.23 30.4 12
200539 22.50 4.05 62 14.39 30.6 12
200955 22.63 4.05 62 14.53 30.7 12
201864 24.60 4.05 62 16.49 32.7 12
201873 25.27 4.05 62 17.16 33.4 12
200810 31.50 4.05 62 23.39 39.6 2
Confidence level used: 0.95
P value adjustment: tukey method for comparing a family of 31 estimates
significance level used: alpha = 0.1 plot(CLD(lsmeans(lm_set_2.1_WS, ~isolate), alpha=0.1), main="All pairwise comparisons for Mean Dry Shoot Weight", xlab = "Mean Weight (mg)", ylab = "Isolate ID")'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead.
2.1.4. - CLD - Mean Estimate Differences to Control
Compact Letter Display of estimates for Dry Shoot Mean weight (mg) differences between plants that were inoculated with a mucilage isolates and the non-inoculated control.
plot(CLD(lsmeans(lm_set_2.1_DS, trt.vs.ctrl~isolate,ref=31)$contrasts, alpha=0.1), main="Mean Dry Shoot Weight Differences of Inoculated Plants vs. Control", xlab = "Difference of Mean Estimates (mg)", ylab = "Contrasts")'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead.No information available to display confidence limits
The mean wet shoot weight for BCW200810 is significantly different from that of the control at an alpha level of 0.1
Batch 2.2
# analyze set 2.2 data
str(set_2.2)'data.frame': 600 obs. of 4 variables:
$ isolate: chr "201152" "201152" "201152" "201152" ...
$ rep : int 1 1 1 1 1 1 2 2 2 2 ...
$ sample : chr "WS" "WR" "DS" "DR" ...
$ mg : num 287.2 43.7 23.8 3.5 48003.8 ...#subset by sample type
set_2.2_DR <- filter(set_2.2, sample == "DR")
set_2.2_DS <- filter(set_2.2, sample == "DS")
set_2.2_WR <- filter(set_2.2, sample == "WR")
set_2.2_WS <- filter(set_2.2, sample == "WS")Experimental Design Table
| Structure | Factor | Type | # levels |
|---|---|---|---|
| Treatment | Isolate | Qualitative | 33 |
| Design | MS Media Slants | Experimental Unit | 99 |
| Response | Plant Tissue Weight | Observational Unit | 396 |
| Response | Dry Root Weight (DR) | Variable | 99 |
| Response | Dry Shoot Weight (DS) | Variable | 99 |
| Response | Wet Root Weight (WR) | Variable | 99 |
| Response | Wet Shoot Weight (WS) | Variable | 99 |
2.2.1. Dry Root Weight
# linear model of dry root data
lm_set_2.2_DR <- lm(mg ~ 1 + isolate, set_2.2_DR)
op = par(mfrow=c(1,2))
plot(lm_set_2.2_DR, which = c(2,3))
par(op)
2.2.1. - Anova
# assess variance - but the data isn't normal
anova(lm_set_2.2_DR)Analysis of Variance Table
Response: mg
Df Sum Sq Mean Sq F value Pr(>F)
isolate 31 264.52 8.5329 1.5173 0.07941 .
Residuals 65 365.53 5.6236
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 12.2.1. - LSMeans - Dunnett
lsm_s2.2.dun.DR <- summary(lsmeans(lm_set_2.2_DR, trt.vs.ctrl ~isolate, ref=32)$contrasts, infer = c(T,T))
write.csv(lsm_s2.2.dun.DR, "./lsmeans_summary_tables/lsm_s2.2.dun.DR.csv")2.2.1. - CLD - (Pairwise by isolate)
CLD(lsmeans(lm_set_2.2_DR, ~isolate), alpha=0.1)'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead. isolate lsmean SE df lower.CL upper.CL .group
200599 0.867 1.37 65 -1.8677 3.60 1
200821 1.900 1.37 65 -0.8344 4.63 1
201901 2.667 1.37 65 -0.0677 5.40 12
201152 2.833 1.37 65 0.0990 5.57 12
200460 3.267 1.37 65 0.5323 6.00 12
200446 3.367 1.37 65 0.6323 6.10 12
200634 3.400 1.37 65 0.6656 6.13 12
200986 3.733 1.37 65 0.9990 6.47 12
201845 3.800 1.37 65 1.0656 6.53 12
200561 3.833 1.37 65 1.0990 6.57 12
201957 3.833 1.37 65 1.0990 6.57 12
control 4.075 1.19 65 1.7070 6.44 12
201083 4.233 1.37 65 1.4990 6.97 12
201926 4.333 1.37 65 1.5990 7.07 12
200266 4.600 1.37 65 1.8656 7.33 12
200473 4.633 1.37 65 1.8990 7.37 12
201079 4.667 1.37 65 1.9323 7.40 12
200665 4.833 1.37 65 2.0990 7.57 12
201254 5.000 1.37 65 2.2656 7.73 12
201185 5.067 1.37 65 2.3323 7.80 12
201808 5.200 1.37 65 2.4656 7.93 12
201260 5.367 1.37 65 2.6323 8.10 12
201098 5.467 1.37 65 2.7323 8.20 12
201896 5.567 1.37 65 2.8323 8.30 12
200648 5.600 1.37 65 2.8656 8.33 12
201175 5.700 1.37 65 2.9656 8.43 12
201056 6.000 1.37 65 3.2656 8.73 12
200883 6.200 1.37 65 3.4656 8.93 12
201915 6.633 1.37 65 3.8990 9.37 12
200547 7.500 1.37 65 4.7656 10.23 12
201176 7.767 1.37 65 5.0323 10.50 12
201849 9.100 1.37 65 6.3656 11.83 2
Confidence level used: 0.95
P value adjustment: tukey method for comparing a family of 32 estimates
significance level used: alpha = 0.1 plot(CLD(lsmeans(lm_set_2.2_DR, ~isolate), alpha=0.1), main="All pairwise comparisons for Mean Dry Root Weight", xlab = "Mean Weight (mg)", ylab = "Isolate ID")'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead.
2.2.1. - CLD - Mean Estimate Differences to Control
Compact Letter Display of estimates for Dry Root Mean weight (mg) differences between plants that were inoculated with a mucilage isolates and the non-inoculated control.
plot(CLD(lsmeans(lm_set_2.2_DR, trt.vs.ctrl~isolate,ref=32)$contrasts, alpha=0.1), main="Mean Dry Root Weight Differences of Inoculated Plants vs. Control", xlab = "Difference of Mean Estimates (mg)", ylab = "Contrasts")'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead.No information available to display confidence limits
BCW201849 has a dry root weight that is significantly higher than the control.
2.2.2. Dry Shoot Weight
# linear model of dry shoot data
lm_set_2.2_DS <- lm(mg ~ 1 + isolate, set_2.2_DS)
op = par(mfrow=c(1,2))
plot(lm_set_2.2_DS, which = c(2,3))
par(op)
2.2.2. - Anova
# assess variance
anova(lm_set_2.2_DS)Analysis of Variance Table
Response: mg
Df Sum Sq Mean Sq F value Pr(>F)
isolate 31 4883.5 157.532 2.2711 0.002753 **
Residuals 65 4508.7 69.364
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 12.2.2. - LSMeans - Dunnett
lsm_s2.2.dun.DS <- summary(lsmeans(lm_set_2.2_DS, trt.vs.ctrl ~isolate, ref=32)$contrasts, infer = c(T,T))
write.csv(lsm_s2.2.dun.DS, "./lsmeans_summary_tables/lsm_s2.2.dun.DS.csv")2.2.2. - CLD - (Pairwise by isolate)
CLD(lsmeans(lm_set_2.2_DS, ~isolate), alpha=0.1)'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead. isolate lsmean SE df lower.CL upper.CL .group
200599 7.8 4.81 65 -1.80 17.4 1
201901 10.8 4.81 65 1.16 20.4 1
200821 11.1 4.81 65 1.50 20.7 1
201056 13.0 4.81 65 3.36 22.6 1
200561 13.0 4.81 65 3.40 22.6 1
control 13.4 4.16 65 5.13 21.8 1
201957 13.5 4.81 65 3.93 23.1 1
200986 13.8 4.81 65 4.16 23.4 1
200665 15.9 4.81 65 6.33 25.5 1
201926 16.9 4.81 65 7.30 26.5 1
201260 17.0 4.81 65 7.36 26.6 1
200460 17.6 4.81 65 8.03 27.2 1
201808 19.1 4.81 65 9.50 28.7 12
201254 20.8 4.81 65 11.16 30.4 12
201098 21.0 4.81 65 11.40 30.6 12
200266 21.5 4.81 65 11.93 31.1 12
200634 21.6 4.81 65 12.03 31.2 12
201152 22.3 4.81 65 12.66 31.9 12
200648 22.5 4.81 65 12.90 32.1 12
201083 22.8 4.81 65 13.20 32.4 12
201185 23.8 4.81 65 14.16 33.4 12
201845 23.9 4.81 65 14.33 33.5 12
201175 24.2 4.81 65 14.56 33.8 12
201079 25.9 4.81 65 16.26 35.5 12
201176 26.6 4.81 65 17.00 36.2 12
201915 27.0 4.81 65 17.36 36.6 12
201896 27.3 4.81 65 17.66 36.9 12
200473 27.6 4.81 65 17.96 37.2 12
200446 28.2 4.81 65 18.63 37.8 12
200547 28.8 4.81 65 19.20 38.4 12
200883 29.3 4.81 65 19.73 38.9 12
201849 42.8 4.81 65 33.20 52.4 2
Confidence level used: 0.95
P value adjustment: tukey method for comparing a family of 32 estimates
significance level used: alpha = 0.1 plot(CLD(lsmeans(lm_set_2.2_DS, ~isolate), alpha=0.1), main="All pairwise comparisons for Mean Dry Shoot Weight", xlab = "Mean Weight (mg)", ylab = "Isolate ID")'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead.
2.2.2. - CLD - Mean Estimate Differences to Control
Compact Letter Display of estimates for Dry Shoot Mean weight (mg) differences between plants that were inoculated with a mucilage isolates and the non-inoculated control.
plot(CLD(lsmeans(lm_set_2.2_DS, trt.vs.ctrl~isolate,ref=32)$contrasts, alpha=0.1), main="Mean Dry Shoot Weight Differences of Inoculated Plants vs. Control", xlab = "Difference of Mean Estimates (mg)", ylab = "Contrasts")'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead.No information available to display confidence limits
BCW201849 also has a mean dry shoot weight that is statistically significant from that of the control at an alpha level of 0.1.
2.2.3. Wet Root Weight
# linear model of wet root data
lm_set_2.2_WR <- lm(mg ~ 1 + isolate, set_2.2_WR)
op = par(mfrow=c(1,2))
plot(lm_set_2.2_WR, which = c(2,3))
par(op)
2.2.3. - Anova
# assess variance
anova(lm_set_2.2_WR)Analysis of Variance Table
Response: mg
Df Sum Sq Mean Sq F value Pr(>F)
isolate 31 51631 1665.5 1.5425 0.07158 .
Residuals 65 70186 1079.8
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 12.2.3. - LSMeans - Dunnett
lsm_s2.2.dun.WR <- summary(lsmeans(lm_set_2.2_WR, trt.vs.ctrl ~isolate, ref=32)$contrasts, infer = c(T,T))
write.csv(lsm_s2.2.dun.WR, "./lsmeans_summary_tables/lsm_s2.2.dun.WR.csv")2.2.3. - CLD - (Pairwise by isolate)
CLD(lsmeans(lm_set_2.2_WR, ~isolate), alpha=0.1)'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead. isolate lsmean SE df lower.CL upper.CL .group
200599 9.33 19.0 65 -28.556 47.2 1
200821 21.27 19.0 65 -16.623 59.2 12
201901 27.77 19.0 65 -10.123 65.7 12
201152 37.50 19.0 65 -0.389 75.4 12
200446 42.50 19.0 65 4.611 80.4 12
200561 42.60 19.0 65 4.711 80.5 12
201845 43.23 19.0 65 5.344 81.1 12
200634 44.33 19.0 65 6.444 82.2 12
201926 45.20 19.0 65 7.311 83.1 12
200986 48.67 19.0 65 10.777 86.6 12
200460 49.30 19.0 65 11.411 87.2 12
200473 52.70 19.0 65 14.811 90.6 12
control 53.05 16.4 65 20.237 85.9 12
201957 54.33 19.0 65 16.444 92.2 12
201260 54.43 19.0 65 16.544 92.3 12
201083 56.13 19.0 65 18.244 94.0 12
200266 56.47 19.0 65 18.577 94.4 12
200665 56.73 19.0 65 18.844 94.6 12
201185 58.60 19.0 65 20.711 96.5 12
201079 59.60 19.0 65 21.711 97.5 12
201808 62.50 19.0 65 24.611 100.4 12
200648 63.97 19.0 65 26.077 101.9 12
201056 65.07 19.0 65 27.177 103.0 12
201254 69.43 19.0 65 31.544 107.3 12
201098 71.00 19.0 65 33.111 108.9 12
201915 72.53 19.0 65 34.644 110.4 12
200883 80.73 19.0 65 42.844 118.6 12
201896 83.53 19.0 65 45.644 121.4 12
201175 92.50 19.0 65 54.611 130.4 12
201176 92.80 19.0 65 54.911 130.7 12
200547 111.70 19.0 65 73.811 149.6 2
201849 119.93 19.0 65 82.044 157.8 2
Confidence level used: 0.95
P value adjustment: tukey method for comparing a family of 32 estimates
significance level used: alpha = 0.1 plot(CLD(lsmeans(lm_set_2.2_WR, ~isolate), alpha=0.1), main="All pairwise comparisons for Mean Wet Root Weight", xlab = "Mean Weight (mg)", ylab = "Isolate ID")'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead.
2.2.3. - CLD - Mean Estimate Differences to Control
Compact Letter Display of estimates for Wet Root Mean weight (mg) differences between plants that were inoculated with a mucilage isolates and the non-inoculated control.
plot(CLD(lsmeans(lm_set_2.2_WR, trt.vs.ctrl~isolate,ref=31)$contrasts, alpha=0.1), main="Mean Wet Root Weight Differences of Inoculated Plants vs. Control", xlab = "Difference of Mean Estimates (mg)", ylab = "Contrasts")'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead.No information available to display confidence limits
None of the isolates have non-overlapping confidence intervals for their wet root weight mean esimates.
2.2.4. Wet Shoot Weight
# linear model of wet shoot data
lm_set_2.2_WS <- lm(mg ~ 1 + isolate, set_2.2_WS)
op = par(mfrow=c(1,2))
plot(lm_set_2.2_WS, which = c(2,3))
par(op)
2.2.4. - Anova
# assess variance
anova(lm_set_2.2_WS)Analysis of Variance Table
Response: mg
Df Sum Sq Mean Sq F value Pr(>F)
isolate 31 1320679 42603 2.264 0.002845 **
Residuals 65 1223127 18817
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 12.2.4. - LSMeans - Dunnett
lsm_s2.2.dun.WS <- summary(lsmeans(lm_set_2.2_WS, trt.vs.ctrl ~isolate, ref=32)$contrasts, infer = c(T,T))
write.csv(lsm_s2.2.dun.WS, "./lsmeans_summary_tables/lsm_s2.2.dun.WS.csv")2.2.4. - CLD - (Pairwise by isolate)
CLD(lsmeans(lm_set_2.2_WS, ~isolate), alpha=0.1)'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead. isolate lsmean SE df lower.CL upper.CL .group
200599 93.6 79.2 65 -64.54 252 1
200821 143.4 79.2 65 -14.77 302 1
201901 143.4 79.2 65 -14.77 302 1
201056 154.3 79.2 65 -3.84 313 1
200561 169.1 79.2 65 10.96 327 1
200986 174.4 79.2 65 16.23 333 1
201957 175.9 79.2 65 17.76 334 1
200665 197.9 79.2 65 39.76 356 1
201260 210.3 79.2 65 52.10 368 1
201926 217.0 79.2 65 58.83 375 1
200460 217.3 79.2 65 59.10 375 1
control 224.8 68.6 65 87.87 362 1
201808 241.3 79.2 65 83.10 399 12
201098 249.0 79.2 65 90.80 407 12
201083 279.4 79.2 65 121.26 438 12
201152 284.5 79.2 65 126.36 443 12
200648 290.5 79.2 65 132.30 449 12
200634 299.6 79.2 65 141.46 458 12
201845 309.5 79.2 65 151.30 468 12
200266 309.8 79.2 65 151.66 468 12
201185 332.0 79.2 65 173.86 490 12
201079 334.8 79.2 65 176.60 493 12
201254 349.1 79.2 65 190.90 507 12
200473 361.2 79.2 65 203.06 519 12
201915 367.2 79.2 65 209.00 525 12
201176 400.5 79.2 65 242.30 559 12
200446 410.6 79.2 65 252.40 569 12
200883 410.7 79.2 65 252.56 569 12
201175 432.6 79.2 65 274.40 591 12
201896 453.9 79.2 65 295.76 612 12
200547 474.0 79.2 65 315.80 632 12
201849 640.1 79.2 65 481.90 798 2
Confidence level used: 0.95
P value adjustment: tukey method for comparing a family of 32 estimates
significance level used: alpha = 0.1 plot(CLD(lsmeans(lm_set_2.2_WS, ~isolate), alpha=0.1), main="All pairwise comparisons for Mean Wet Shoot Weight", xlab = "Mean Weight (mg)", ylab = "Isolate ID")'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead.
2.2.4. - CLD - Mean Estimate Differences to Control
Compact Letter Display of estimates for Wet Shoot Mean weight (mg) differences between plants that were inoculated with a mucilage isolates and the non-inoculated control.
plot(CLD(lsmeans(lm_set_2.2_WS, trt.vs.ctrl~isolate,ref=32)$contrasts, alpha=0.1), main="Mean Wet Shoot Weight Differences of Inoculated Plants vs. Control", xlab = "Difference of Mean Estimates (mg)", ylab = "Contrasts")'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead.No information available to display confidence limits
BCW201849 has a mean wet shoot weight that is statistically significant from that of the control at an alpha level of 0.1
Batch 2.3
# analyze set 2.3 data
str(set_2.3)'data.frame': 600 obs. of 4 variables:
$ isolate: chr "200449" "200449" "200449" "200449" ...
$ rep : int 1 1 1 1 1 1 2 2 2 2 ...
$ sample : chr "WS" "WR" "DS" "DR" ...
$ mg : num 180.6 41.8 18.8 4 49207.1 ...#subset by sample type
set_2.3_DR <- filter(set_2.3, sample == "DR")
set_2.3_DS <- filter(set_2.3, sample == "DS")
set_2.3_WR <- filter(set_2.3, sample == "WR")
set_2.3_WS <- filter(set_2.3, sample == "WS")Experimental Design Table
| Structure | Factor | Type | # levels |
|---|---|---|---|
| Treatment | Isolate | Qualitative | 29 |
| Design | MS Media Slants | Experimental Unit | 87 |
| Response | Plant Tissue Weight | Observational Unit | 348 |
| Response | Dry Root Weight (DR) | Variable | 87 |
| Response | Dry Shoot Weight (DS) | Variable | 87 |
| Response | Wet Root Weight (WR) | Variable | 87 |
| Response | Wet Shoot Weight (WS) | Variable | 87 |
2.3.1. Dry Root Weight
# linear model of dry root data
lm_set_2.3_DR <- lm(mg ~ 1 + isolate, set_2.3_DR)
op = par(mfrow=c(1,2))
plot(lm_set_2.3_DR, which = c(2,3))not plotting observations with leverage one:
36par(op)
2.3.1. - Anova
# assess variance
anova(lm_set_2.3_DR)Analysis of Variance Table
Response: mg
Df Sum Sq Mean Sq F value Pr(>F)
isolate 25 229.94 9.1975 0.7591 0.7698
Residuals 50 605.78 12.1156 There appears to be no significant differences among the fitted values based on the anova.
2.3.1. - LSMeans - Dunnett
lsm_s2.3.dun.DR <- summary(lsmeans(lm_set_2.3_DR, trt.vs.ctrl ~isolate, ref=26)$contrasts, infer = c(T,T))
write.csv(lsm_s2.3.dun.DR, "./lsmeans_summary_tables/lsm_s2.3.dun.DR.csv")2.3.1. - CLD - (Pairwise by isolate)
CLD(lsmeans(lm_set_2.3_DR, ~isolate), alpha=0.1)'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead. isolate lsmean SE df lower.CL upper.CL .group
201847 2.80 2.01 50 -1.236 6.84 1
201877 2.83 2.01 50 -1.203 6.87 1
201302 3.20 2.01 50 -0.836 7.24 1
200669 3.37 2.01 50 -0.670 7.40 1
200449 3.47 2.01 50 -0.570 7.50 1
201838 3.60 2.01 50 -0.436 7.64 1
201350 3.70 2.01 50 -0.336 7.74 1
200724 4.17 2.01 50 0.130 8.20 1
control 4.33 1.74 50 0.829 7.82 1
200607 4.37 2.01 50 0.330 8.40 1
201826 4.87 2.01 50 0.830 8.90 1
200517 4.97 2.01 50 0.930 9.00 1
201871 5.07 2.01 50 1.030 9.10 1
201848 5.23 2.01 50 1.197 9.27 1
200600 5.27 2.01 50 1.230 9.30 1
201812 5.40 3.48 50 -1.591 12.39 1
200529 5.47 2.01 50 1.430 9.50 1
200307 6.13 2.01 50 2.097 10.17 1
200274 6.17 2.01 50 2.130 10.20 1
201881 6.27 2.01 50 2.230 10.30 1
200736 6.35 2.46 50 1.406 11.29 1
201972 7.03 2.01 50 2.997 11.07 1
200567 7.13 2.01 50 3.097 11.17 1
201703 7.43 2.01 50 3.397 11.47 1
200885 7.93 2.01 50 3.897 11.97 1
200938 10.07 2.01 50 6.030 14.10 1
Confidence level used: 0.95
P value adjustment: tukey method for comparing a family of 26 estimates
significance level used: alpha = 0.1 plot(CLD(lsmeans(lm_set_2.3_DR, ~isolate), alpha=0.1), main="All pairwise comparisons for Mean Dry Root Weight", xlab = "Mean Weight (mg)", ylab = "Isolate ID")'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead.
2.3.1. - CLD - Mean Estimate Differences to Control
Compact Letter Display of estimates for Dry Root Mean weight (mg) differences between plants that were inoculated with a mucilage isolates and the non-inoculated control.
plot(CLD(lsmeans(lm_set_2.3_DR, trt.vs.ctrl~isolate,ref=26)$contrasts, alpha=0.1), main="Mean Dry Root Weight Differences of Inoculated Plants vs. Control", xlab = "Difference of Mean Estimates (mg)", ylab = "Contrasts")'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead.No information available to display confidence limits
BCW201881 has a higher mean estimate for dry root weight than the control with non-overlapping confidence intervals.
2.3.2. Dry Shoot Weight
# linear model of dry shoot data
lm_set_2.3_DS <- lm(mg ~ 1 + isolate, set_2.3_DS)
op = par(mfrow=c(1,2))
plot(lm_set_2.3_DS, which = c(2,3))not plotting observations with leverage one:
36par(op)
2.3.2. - Anova
# assess variance
anova(lm_set_2.3_DS)Analysis of Variance Table
Response: mg
Df Sum Sq Mean Sq F value Pr(>F)
isolate 25 1588.1 63.525 0.6598 0.8693
Residuals 50 4813.7 96.275 2.3.2. - LSMeans - Dunnett
lsm_s2.3.dun.DS <- summary(lsmeans(lm_set_2.3_DS, trt.vs.ctrl ~isolate, ref=26)$contrasts, infer = c(T,T))
write.csv(lsm_s2.3.dun.DS, "./lsmeans_summary_tables/lsm_s2.3.dun.DS.csv")2.3.2. - CLD - (Pairwise by isolate)
CLD(lsmeans(lm_set_2.3_DS, ~isolate), alpha=0.1)'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead. isolate lsmean SE df lower.CL upper.CL .group
201847 13.0 5.66 50 1.588 24.3 1
200529 19.3 5.66 50 7.955 30.7 1
200885 19.4 5.66 50 8.022 30.8 1
200449 20.3 5.66 50 8.888 31.6 1
201812 20.5 9.81 50 0.792 40.2 1
200307 20.7 5.66 50 9.355 32.1 1
201871 20.8 5.66 50 9.455 32.2 1
200517 20.9 5.66 50 9.488 32.2 1
201877 21.1 5.66 50 9.688 32.4 1
200607 21.3 5.66 50 9.922 32.7 1
201848 21.4 5.66 50 10.022 32.8 1
201302 21.5 5.66 50 10.155 32.9 1
200600 22.0 5.66 50 10.622 33.4 1
201703 22.1 5.66 50 10.688 33.4 1
control 22.4 4.91 50 12.496 32.2 1
200669 22.6 5.66 50 11.255 34.0 1
201350 23.6 5.66 50 12.255 35.0 1
200736 24.2 6.94 50 10.314 38.2 1
201838 24.3 5.66 50 12.955 35.7 1
201826 24.4 5.66 50 13.022 35.8 1
200274 25.0 5.66 50 13.622 36.4 1
201881 26.2 5.66 50 14.855 37.6 1
200567 29.8 5.66 50 18.422 41.2 1
200724 30.7 5.66 50 19.288 42.0 1
201972 32.6 5.66 50 21.222 44.0 1
200938 35.3 5.66 50 23.888 46.6 1
Confidence level used: 0.95
P value adjustment: tukey method for comparing a family of 26 estimates
significance level used: alpha = 0.1 plot(CLD(lsmeans(lm_set_2.3_DS, ~isolate), alpha=0.1), main="All pairwise comparisons for Mean Dry Shoot Weight", xlab = "Mean Weight (mg)", ylab = "Isolate ID")'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead.
2.3.2. - CLD - Mean Estimate Differences to Control
Compact Letter Display of estimates for Dry Shoot Mean weight (mg) differences between plants that were inoculated with a mucilage isolates and the non-inoculated control.
plot(CLD(lsmeans(lm_set_2.3_DS, trt.vs.ctrl~isolate,ref=26)$contrasts, alpha=0.1), main="Mean Dry Shoot Weight Differences of Inoculated Plants vs. Control", xlab = "Difference of Mean Estimates (mg)", ylab = "Contrasts")'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead.No information available to display confidence limits
All of the isolates have means with confidence intervals that overlap with that of the control for set 2.3
2.3.3. Wet Root Weight
# linear model of wet root data
lm_set_2.3_WR <- lm(mg ~ 1 + isolate, set_2.3_WR)
op = par(mfrow=c(1,2))
plot(lm_set_2.3_WR, which = c(2,3))not plotting observations with leverage one:
36par(op)
2.3.3 - Anova
# assess variance
anova(lm_set_2.3_WR)Analysis of Variance Table
Response: mg
Df Sum Sq Mean Sq F value Pr(>F)
isolate 25 28986 1159.4 0.7627 0.766
Residuals 50 76013 1520.3 2.3.3. - LSMeans - Dunnett
lsm_s2.3.dun.WR <- summary(lsmeans(lm_set_2.3_WR, trt.vs.ctrl ~isolate, ref=26)$contrasts, infer = c(T,T))
write.csv(lsm_s2.3.dun.WR, "./lsmeans_summary_tables/lsm_s2.3.dun.WR.csv")2.3.3. - CLD - (Pairwise by isolate)
CLD(lsmeans(lm_set_2.3_WR, ~isolate), alpha=0.1)'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead. isolate lsmean SE df lower.CL upper.CL .group
201847 32.8 22.5 50 -12.382 78.0 1
200938 35.8 22.5 50 -9.415 81.0 1
200669 36.9 22.5 50 -8.315 82.1 1
201877 37.2 22.5 50 -8.015 82.4 1
201302 37.8 22.5 50 -7.415 83.0 1
200449 38.0 22.5 50 -7.182 83.2 1
201838 45.3 22.5 50 0.118 90.5 1
201350 50.9 22.5 50 5.685 96.1 1
200724 54.2 22.5 50 8.952 99.4 1
200607 56.7 22.5 50 11.518 101.9 1
200517 57.3 22.5 50 12.118 102.5 1
201812 57.4 39.0 50 -20.915 135.7 1
200600 59.2 22.5 50 13.952 104.4 1
200274 62.4 22.5 50 17.152 107.6 1
201826 63.2 22.5 50 18.018 108.4 1
201871 63.3 22.5 50 18.118 108.5 1
201848 65.0 22.5 50 19.785 110.2 1
200529 67.3 22.5 50 22.052 112.5 1
control 70.2 19.5 50 31.043 109.4 1
200736 74.7 27.6 50 19.323 130.1 1
201881 74.7 22.5 50 29.518 119.9 1
201703 77.2 22.5 50 31.952 122.4 1
200307 80.7 22.5 50 35.518 125.9 1
201972 89.5 22.5 50 44.318 134.7 1
200567 93.8 22.5 50 48.552 139.0 1
200885 111.6 22.5 50 66.352 156.8 1
Confidence level used: 0.95
P value adjustment: tukey method for comparing a family of 26 estimates
significance level used: alpha = 0.1 plot(CLD(lsmeans(lm_set_2.3_WR, ~isolate), alpha=0.1), main="All pairwise comparisons for Mean Wet Root Weight", xlab = "Mean Weight (mg)", ylab = "Isolate ID")'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead.
2.3.3. - CLD - Mean Estimate Differences to Control
Compact Letter Display of estimates for Wet Root Mean weight (mg) differences between plants that were inoculated with a mucilage isolates and the non-inoculated control.
plot(CLD(lsmeans(lm_set_2.3_WR, trt.vs.ctrl~isolate,ref=26)$contrasts, alpha=0.1), main="Mean Wet Root Weight Differences of Inoculated Plants vs. Control", xlab = "Difference of Mean Estimates (mg)", ylab = "Contrasts")'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead.No information available to display confidence limits
Wet Root data shows all isolate mean estimates have Confidence Intervals that overlap.
2.3.4. Wet Shoot Weight
# linear model of Wet shoot data
lm_set_2.3_WS <- lm(mg ~ 1 + isolate, set_2.3_WS)
op = par(mfrow=c(1,2))
plot(lm_set_2.3_WS, which = c(2,3))not plotting observations with leverage one:
36par(op)
2.3.4. - Anova
# assess variance
anova(lm_set_2.3_WS)Analysis of Variance Table
Response: mg
Df Sum Sq Mean Sq F value Pr(>F)
isolate 25 404716 16189 0.7062 0.8258
Residuals 50 1146222 22924 Anova shows similar result to dry shoot.
2.3.4. - LSMeans - Dunnett
lsm_s2.3.dun.WS <- summary(lsmeans(lm_set_2.3_WS, trt.vs.ctrl ~isolate, ref=26)$contrasts, infer = c(T,T))
write.csv(lsm_s2.3.dun.WS, "./lsmeans_summary_tables/lsm_s2.3.dun.WS.csv")2.3.4. - CLD - (Pairwise by isolate)
CLD(lsmeans(lm_set_2.3_DS, ~isolate), alpha=0.1)'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead. isolate lsmean SE df lower.CL upper.CL .group
201847 13.0 5.66 50 1.588 24.3 1
200529 19.3 5.66 50 7.955 30.7 1
200885 19.4 5.66 50 8.022 30.8 1
200449 20.3 5.66 50 8.888 31.6 1
201812 20.5 9.81 50 0.792 40.2 1
200307 20.7 5.66 50 9.355 32.1 1
201871 20.8 5.66 50 9.455 32.2 1
200517 20.9 5.66 50 9.488 32.2 1
201877 21.1 5.66 50 9.688 32.4 1
200607 21.3 5.66 50 9.922 32.7 1
201848 21.4 5.66 50 10.022 32.8 1
201302 21.5 5.66 50 10.155 32.9 1
200600 22.0 5.66 50 10.622 33.4 1
201703 22.1 5.66 50 10.688 33.4 1
control 22.4 4.91 50 12.496 32.2 1
200669 22.6 5.66 50 11.255 34.0 1
201350 23.6 5.66 50 12.255 35.0 1
200736 24.2 6.94 50 10.314 38.2 1
201838 24.3 5.66 50 12.955 35.7 1
201826 24.4 5.66 50 13.022 35.8 1
200274 25.0 5.66 50 13.622 36.4 1
201881 26.2 5.66 50 14.855 37.6 1
200567 29.8 5.66 50 18.422 41.2 1
200724 30.7 5.66 50 19.288 42.0 1
201972 32.6 5.66 50 21.222 44.0 1
200938 35.3 5.66 50 23.888 46.6 1
Confidence level used: 0.95
P value adjustment: tukey method for comparing a family of 26 estimates
significance level used: alpha = 0.1 plot(CLD(lsmeans(lm_set_2.3_DS, ~isolate), alpha=0.1), main="All pairwise comparisons for Mean Wet Shoot Weight", xlab = "Mean Weight (mg)", ylab = "Isolate ID")'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead.
2.3.4. - CLD - Mean Estimate Differences to Control
Compact Letter Display of estimates for Wet Shoot Mean weight (mg) differences between plants that were inoculated with a mucilage isolates and the non-inoculated control.
plot(CLD(lsmeans(lm_set_2.3_DS, trt.vs.ctrl~isolate,ref=26)$contrasts, alpha=0.1), main="Mean Wet Shoot Weight Differences of Inoculated Plants vs. Control", xlab = "Difference of Mean Estimates (mg)", ylab = "Contrasts")'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead.No information available to display confidence limits
All isolates means and CI’s overlap with the control.
Batch 2.4
# analyze set 2.4 data
str(set_2.4)'data.frame': 672 obs. of 4 variables:
$ isolate: chr "201814" "201814" "201814" "201814" ...
$ rep : int 1 1 1 1 1 1 2 2 2 2 ...
$ sample : chr "WS" "WR" "DS" "DR" ...
$ mg : num 403.7 43.8 30.3 4.6 47528.7 ...#subset by sample type
set_2.4_DR <- filter(set_2.4, sample == "DR")
set_2.4_DS <- filter(set_2.4, sample == "DS")
set_2.4_WR <- filter(set_2.4, sample == "WR")
set_2.4_WS <- filter(set_2.4, sample == "WS")Experimental Design Table
| Structure | Factor | Type | # levels |
|---|---|---|---|
| Treatment | Isolate | Qualitative | 37 |
| Design | MS Media Slants | Experimental Unit | 111 |
| Response | Plant Tissue Weight | Observational Unit | 444 |
| Response | Dry Root Weight (DR) | Variable | 111 |
| Response | Dry Shoot Weight (DS) | Variable | 111 |
| Response | Wet Root Weight (WR) | Variable | 111 |
| Response | Wet Shoot Weight (WS) | Variable | 111 |
2.4.1. Dry Root Weight
# linear model of dry root data for set 2.4
lm_set_2.4_DR <- lm(mg ~ 1 + isolate, set_2.4_DR)
op = par(mfrow=c(1,2))
plot(lm_set_2.4_DR, which = c(2,3))
par(op)
2.4.1. - Anova
# assess variance
anova(lm_set_2.4_DR)Analysis of Variance Table
Response: mg
Df Sum Sq Mean Sq F value Pr(>F)
isolate 35 160.20 4.5772 1.9855 0.007161 **
Residuals 72 165.98 2.3053
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 12.4.1. - LSMeans - Dunnett
lsm_s2.4.dun.DR <- summary(lsmeans(lm_set_2.4_DR, trt.vs.ctrl ~isolate, ref=36)$contrasts, infer = c(T,T))
write.csv(lsm_s2.4.dun.DR, "./lsmeans_summary_tables/lsm_s2.4.dun.DR.csv")2.4.1. - CLD - (Pairwise by isolate)
CLD(lsmeans(lm_set_2.4_DR, ~isolate), alpha=0.1)'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead. isolate lsmean SE df lower.CL upper.CL .group
200598 1.97 0.877 72 0.2192 3.71 1
200882 2.20 1.074 72 0.0598 4.34 1
201151 2.30 0.877 72 0.5525 4.05 1
201097 2.57 0.877 72 0.8192 4.31 1
201872 2.63 0.877 72 0.8859 4.38 1
201833 2.70 0.877 72 0.9525 4.45 1
200570 2.77 0.877 72 1.0192 4.51 1
201263 2.83 0.877 72 1.0859 4.58 1
200552 2.90 0.877 72 1.1525 4.65 1
control 3.00 0.759 72 1.4866 4.51 1
201019 3.17 0.877 72 1.4192 4.91 1
200722 3.17 0.877 72 1.4192 4.91 1
200920 3.17 0.877 72 1.4192 4.91 1
201850 3.27 0.877 72 1.5192 5.01 1
201827 3.30 0.877 72 1.5525 5.05 1
200718 3.30 0.877 72 1.5525 5.05 1
201853 3.40 0.877 72 1.6525 5.15 1
200565 3.40 0.877 72 1.6525 5.15 1
201832 3.47 0.877 72 1.7192 5.21 1
201865 3.57 0.877 72 1.8192 5.31 1
201150 3.60 0.877 72 1.8525 5.35 1
201290 3.60 0.877 72 1.8525 5.35 1
200705 3.63 0.877 72 1.8859 5.38 1
200991 3.70 0.877 72 1.9525 5.45 1
201939 4.37 0.877 72 2.6192 6.11 12
201184 4.40 0.877 72 2.6525 6.15 12
200470 4.40 0.877 72 2.6525 6.15 12
200828 4.50 0.877 72 2.7525 6.25 12
200642 4.60 0.877 72 2.8525 6.35 12
200620 4.77 0.877 72 3.0192 6.51 12
201814 5.00 0.877 72 3.2525 6.75 12
200904 5.00 0.877 72 3.2525 6.75 12
200847 5.03 0.877 72 3.2859 6.78 12
201819 5.27 0.877 72 3.5192 7.01 12
200487 5.47 0.877 72 3.7192 7.21 12
201900 8.63 0.877 72 6.8859 10.38 2
Confidence level used: 0.95
P value adjustment: tukey method for comparing a family of 36 estimates
significance level used: alpha = 0.1 plot(CLD(lsmeans(lm_set_2.4_DR, ~isolate), alpha=0.1), main="All pairwise comparisons for Mean Dry Root Weight", xlab = "Mean Weight (mg)", ylab = "Isolate ID")'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead.
2.4.1. - CLD - Mean Estimate Differences to Control
Compact Letter Display of estimates for Dry Root Mean weight (mg) differences between plants that were inoculated with a mucilage isolates and the non-inoculated control.
plot(CLD(lsmeans(lm_set_2.4_DR, trt.vs.ctrl~isolate,ref=36)$contrasts, alpha=0.1), main="Mean Dry Root Weight Differences of Inoculated Plants vs. Control", xlab = "Difference of Mean Estimates (mg)", ylab = "Contrasts")'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead.No information available to display confidence limits
BCW201900 has a mean dry root weight with non-overlapping confidence intervals relative to those of the control, when compared at an alpha level of 0.1. The mean of 8.63 mg is nearly three times higher than that of the control, which is 3.0 mg.
2.4.2. Dry Shoot Weight
# linear model of dry shoot data from set 2.4
lm_set_2.4_DS <- lm(mg ~ 1 + isolate, set_2.4_DS)
op = par(mfrow=c(1,2))
plot(lm_set_2.4_DS, which = c(2,3))
par(op)
2.4.2. - Anova
# assess variance
anova(lm_set_2.4_DS)Analysis of Variance Table
Response: mg
Df Sum Sq Mean Sq F value Pr(>F)
isolate 35 4865.1 139.002 3.0942 2.553e-05 ***
Residuals 72 3234.5 44.923
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 12.4.2. - LSMeans - Dunnett
lsm_s2.4.dun.DS <- summary(lsmeans(lm_set_2.4_DS, trt.vs.ctrl ~isolate, ref=36)$contrasts, infer = c(T,T))
write.csv(lsm_s2.4.dun.DS, "./lsmeans_summary_tables/lsm_s2.4.dun.DS.csv")2.4.2. - CLD - (Pairwise by isolate)
CLD(lsmeans(lm_set_2.4_DS, ~isolate), alpha=0.1)'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead. isolate lsmean SE df lower.CL upper.CL .group
200882 11.5 4.74 72 2.05 20.9 1
200565 12.4 3.87 72 4.69 20.1 1
201833 12.6 3.87 72 4.92 20.3 1
200722 12.7 3.87 72 4.99 20.4 1
201151 14.4 3.87 72 6.69 22.1 1
200598 14.7 3.87 72 6.99 22.4 1
200642 15.8 3.87 72 8.05 23.5 1
control 16.0 3.35 72 9.29 22.7 1
200470 16.3 3.87 72 8.59 24.0 1
201290 16.6 3.87 72 8.92 24.3 1
201097 16.7 3.87 72 9.02 24.4 1
200705 16.9 3.87 72 9.19 24.6 1
200828 17.2 3.87 72 9.45 24.9 1
201853 17.4 3.87 72 9.69 25.1 1
200552 18.1 3.87 72 10.42 25.8 1
200991 18.2 3.87 72 10.49 25.9 1
201939 18.5 3.87 72 10.75 26.2 1
201819 18.5 3.87 72 10.79 26.2 1
201850 18.8 3.87 72 11.09 26.5 1
201832 19.5 3.87 72 11.79 27.2 1
201872 19.9 3.87 72 12.19 27.6 12
200570 20.8 3.87 72 13.12 28.5 12
201827 20.9 3.87 72 13.22 28.6 12
201184 21.1 3.87 72 13.39 28.8 12
200920 22.5 3.87 72 14.79 30.2 123
200904 23.2 3.87 72 15.52 30.9 123
200718 23.2 3.87 72 15.52 30.9 123
200620 24.2 3.87 72 16.52 31.9 123
201263 24.4 3.87 72 16.65 32.1 123
200847 26.4 3.87 72 18.69 34.1 123
200487 26.5 3.87 72 18.79 34.2 123
201019 27.8 3.87 72 20.05 35.5 123
201150 28.6 3.87 72 20.85 36.3 123
201865 29.1 3.87 72 21.42 36.8 123
201814 40.2 3.87 72 32.45 47.9 23
201900 41.9 3.87 72 34.19 49.6 3
Confidence level used: 0.95
P value adjustment: tukey method for comparing a family of 36 estimates
significance level used: alpha = 0.1 plot(CLD(lsmeans(lm_set_2.4_DS, ~isolate), alpha=0.1), main="All pairwise comparisons for Mean Dry Shoot Weight", xlab = "Mean Weight (mg)", ylab = "Isolate ID")'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead.
2.4.2. - CLD - Mean Estimate Differences to Control
Compact Letter Display of estimates for Dry Shoot Mean weight (mg) differences between plants that were inoculated with a mucilage isolates and the non-inoculated control.
plot(CLD(lsmeans(lm_set_2.4_DS, trt.vs.ctrl~isolate,ref=36)$contrasts, alpha=0.1), main="Mean Dry Shoot Weight Differences of Inoculated Plants vs. Control", xlab = "Difference of Mean Estimates (mg)", ylab = "Contrasts")'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead.No information available to display confidence limits
BCW201814 and BCW201900 both have means with confidence intervals that do not overlap with the mean of the control for isolate set 2.4 (at an alpha level of 0.1)
2.4.3. Wet Root Weight
# linear model of Wet Root data from set 2.4
lm_set_2.4_WR <- lm(mg ~ 1 + isolate, set_2.4_WR)
op = par(mfrow=c(1,2))
plot(lm_set_2.4_WR, which = c(2,3))
par(op)
2.4.3. - Anova
# assess variance
anova(lm_set_2.4_WR)Analysis of Variance Table
Response: mg
Df Sum Sq Mean Sq F value Pr(>F)
isolate 35 32418 926.22 1.7315 0.02512 *
Residuals 72 38516 534.94
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 12.4.3. - LSMeans - Dunnett
lsm_s2.4.dun.WR <- summary(lsmeans(lm_set_2.4_WR, trt.vs.ctrl ~isolate, ref=36)$contrasts, infer = c(T,T))
write.csv(lsm_s2.4.dun.WR, "./lsmeans_summary_tables/lsm_s2.4.dun.WR.csv")2.4.3. - CLD - (Pairwise by isolate)
CLD(lsmeans(lm_set_2.4_WR, ~isolate), alpha=0.1)'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead. isolate lsmean SE df lower.CL upper.CL .group
200470 23.2 13.4 72 -3.419 49.8 1
201151 23.9 13.4 72 -2.686 50.6 1
200598 25.6 13.4 72 -1.053 52.2 1
201833 26.9 13.4 72 0.281 53.5 1
201097 27.0 13.4 72 0.347 53.6 1
200882 28.4 16.4 72 -4.152 61.1 1
200570 29.0 13.4 72 2.347 55.6 1
201019 30.5 13.4 72 3.914 57.2 1
201853 31.3 13.4 72 4.714 58.0 1
200722 34.0 13.4 72 7.381 60.6 1
200565 36.8 13.4 72 10.147 63.4 1
201263 37.2 13.4 72 10.581 63.8 1
201850 37.2 13.4 72 10.614 63.9 1
200552 37.5 13.4 72 10.881 64.1 1
control 41.4 11.6 72 18.297 64.4 1
200920 43.0 13.4 72 16.347 69.6 1
201865 43.3 13.4 72 16.647 69.9 1
201832 43.4 13.4 72 16.781 70.0 1
201827 43.8 13.4 72 17.181 70.4 1
200705 44.3 13.4 72 17.647 70.9 1
200991 45.3 13.4 72 18.681 71.9 1
201184 45.5 13.4 72 18.914 72.2 1
201290 45.9 13.4 72 19.314 72.6 1
201150 48.2 13.4 72 21.614 74.9 1
200718 48.4 13.4 72 21.747 75.0 1
201872 48.7 13.4 72 22.114 75.4 1
200642 51.8 13.4 72 25.214 78.5 12
200620 52.6 13.4 72 25.981 79.2 12
201814 53.6 13.4 72 27.014 80.3 12
200828 59.5 13.4 72 32.847 86.1 12
201939 59.9 13.4 72 33.281 86.5 12
200847 60.0 13.4 72 33.414 86.7 12
201819 60.3 13.4 72 33.647 86.9 12
200904 61.7 13.4 72 35.081 88.3 12
200487 66.3 13.4 72 39.681 92.9 12
201900 121.1 13.4 72 94.447 147.7 2
Confidence level used: 0.95
P value adjustment: tukey method for comparing a family of 36 estimates
significance level used: alpha = 0.1 plot(CLD(lsmeans(lm_set_2.4_WR, ~isolate), alpha=0.1), main="All pairwise comparisons for Mean Wet Root Weight", xlab = "Mean Weight (mg)", ylab = "Isolate ID")'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead.
2.4.3. - CLD - Mean Estimate Differences to Control
Compact Letter Display of estimates for Wet Root Mean weight (mg) differences between plants that were inoculated with a mucilage isolates and the non-inoculated control.
plot(CLD(lsmeans(lm_set_2.4_WR, trt.vs.ctrl~isolate,ref=36)$contrasts, alpha=0.1), main="Mean Wet Root Weight Differences of Inoculated Plants vs. Control", xlab = "Difference of Mean Estimates (mg)", ylab = "Contrasts")'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead.No information available to display confidence limits
BCW201900 wet root mean and associated CI do not overlap with those of the control, and the mean is three times that of the control.
2.4.4. Wet Shoot Weight
# linear model of Wet Shoot data from set 2.4
lm_set_2.4_WS <- lm(mg ~ 1 + isolate, set_2.4_WS)
op = par(mfrow=c(1,2))
plot(lm_set_2.4_WS, which = c(2,3))
par(op)
2.4.4. - Anova
# assess variance
anova(lm_set_2.4_WS)Analysis of Variance Table
Response: mg
Df Sum Sq Mean Sq F value Pr(>F)
isolate 35 1086235 31035.3 3.5606 2.604e-06 ***
Residuals 72 627582 8716.4
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 12.4.4. - LSMeans - Dunnett
lsm_s2.4.dun.WS <- summary(lsmeans(lm_set_2.4_WS, trt.vs.ctrl ~isolate, ref=36)$contrasts, infer = c(T,T))
write.csv(lsm_s2.4.dun.WS, "./lsmeans_summary_tables/lsm_s2.4.dun.WS.csv")2.4.4. - CLD - (Pairwise by isolate)
CLD(lsmeans(lm_set_2.4_WS, ~isolate), alpha=0.1)'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead. isolate lsmean SE df lower.CL upper.CL .group
200565 134 53.9 72 26.2 241 1
200598 153 53.9 72 45.1 260 1
200882 157 66.0 72 25.0 288 1
201833 157 53.9 72 49.5 264 1
200722 161 53.9 72 54.0 269 1
201151 171 53.9 72 63.5 278 1
200470 182 53.9 72 74.7 290 1
201097 190 53.9 72 82.3 297 1
200642 200 53.9 72 92.7 308 1
201850 206 53.9 72 98.3 313 1
201290 206 53.9 72 98.6 314 1
200570 208 53.9 72 101.0 316 1
200552 213 53.9 72 105.8 321 1
200991 218 53.9 72 111.0 326 12
201939 220 53.9 72 112.2 327 12
201853 221 53.9 72 113.5 328 12
200705 221 53.9 72 113.6 329 12
200828 231 53.9 72 123.7 339 12
201819 233 53.9 72 125.9 341 12
201184 241 53.9 72 133.1 348 12
201832 241 53.9 72 133.1 348 12
control 255 46.7 72 161.9 348 12
200904 257 53.9 72 149.9 365 12
201263 267 53.9 72 159.2 374 12
201872 267 53.9 72 159.6 375 12
200620 277 53.9 72 169.7 385 12
200920 282 53.9 72 175.0 390 12
201827 300 53.9 72 192.3 407 12
200718 308 53.9 72 200.1 415 12
201019 309 53.9 72 201.6 416 12
200487 315 53.9 72 207.9 423 12
200847 320 53.9 72 212.5 427 12
201865 391 53.9 72 283.9 499 123
201150 406 53.9 72 298.6 514 123
201814 503 53.9 72 395.8 611 23
201900 652 53.9 72 544.5 759 3
Confidence level used: 0.95
P value adjustment: tukey method for comparing a family of 36 estimates
significance level used: alpha = 0.1 plot(CLD(lsmeans(lm_set_2.4_WS, ~isolate), alpha=0.1), main="All pairwise comparisons for Mean Wet Shoot Weight", xlab = "Mean Weight (mg)", ylab = "Isolate ID")'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead.
2.4.4. - CLD - Mean Estimate Differences to Control
Compact Letter Display of estimates for Wet Shoot Mean weight (mg) differences between plants that were inoculated with a mucilage isolates and the non-inoculated control.
plot(CLD(lsmeans(lm_set_2.4_WS, trt.vs.ctrl~isolate,ref=36)$contrasts, alpha=0.1), main="Mean Wet Shoot Weight Differences of Inoculated Plants vs. Control", xlab = "Difference of Mean Estimates (mg)", ylab = "Contrasts")'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead.No information available to display confidence limits
BCW201900 and BCW201814 are the only isolates that have mean wet shoot weight estimates with confidence intervals that do not overlap with those of the control.
Batch 3.1
# analyze set 3.1 data
str(set_3.1)'data.frame': 672 obs. of 4 variables:
$ isolate: chr "201315" "201315" "201315" "201315" ...
$ rep : int 1 1 1 1 1 1 2 2 2 2 ...
$ sample : chr "WS" "WR" "DS" "DR" ...
$ mg : num 610.6 109.8 30.2 8.3 58879.5 ...#subset by sample type
set_3.1_DR <- filter(set_3.1, sample == "DR")
set_3.1_DS <- filter(set_3.1, sample == "DS")
set_3.1_WR <- filter(set_3.1, sample == "WR")
set_3.1_WS <- filter(set_3.1, sample == "WS")Experimental Design Table
| Structure | Factor | Type | # levels |
|---|---|---|---|
| Treatment | Isolate | Qualitative | 38 |
| Design | MS Media Slants | Experimental Unit | 114 |
| Response | Plant Tissue Weight | Observational Unit | 456 |
| Response | Dry Root Weight (DR) | Variable | 114 |
| Response | Dry Shoot Weight (DS) | Variable | 114 |
| Response | Wet Root Weight (WR) | Variable | 114 |
| Response | Wet Shoot Weight (WS) | Variable | 114 |
3.1.1. Dry Root Weight
# linear model of dry root data
lm_set_3.1_DR <- lm(mg ~ 1 + isolate, set_3.1_DR)
op = par(mfrow=c(1,2))
plot(lm_set_3.1_DR, which = c(2,3))not plotting observations with leverage one:
66par(op)
3.1.1. - Anova
# assess variance
anova(lm_set_3.1_DR)Analysis of Variance Table
Response: mg
Df Sum Sq Mean Sq F value Pr(>F)
isolate 36 125.97 3.4993 1.3324 0.1516
Residuals 70 183.84 2.6263 3.1.1. - LSMeans - Dunnett
lsm_s3.1.dun.DR <- summary(lsmeans(lm_set_3.1_DR, trt.vs.ctrl ~isolate, ref=37)$contrasts, infer = c(T,T))
write.csv(lsm_s3.1.dun.DR, "./lsmeans_summary_tables/lsm_s3.1.dun.DR.csv")3.1.1. - CLD - (Pairwise by isolate)
CLD(lsmeans(lm_set_3.1_DR, ~isolate), alpha=0.1)'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead. isolate lsmean SE df lower.CL upper.CL .group
200290 0.633 0.936 70 -1.2327 2.50 1
201902 1.033 0.936 70 -0.8327 2.90 1
200806 1.067 0.936 70 -0.7994 2.93 1
201982 1.467 0.936 70 -0.3994 3.33 1
201938 1.633 0.936 70 -0.2327 3.50 12
200952 1.667 0.936 70 -0.1994 3.53 12
200588 1.700 1.621 70 -1.5321 4.93 12
200525 1.733 0.936 70 -0.1327 3.60 12
200667 1.767 0.936 70 -0.0994 3.63 12
200881 1.800 0.936 70 -0.0661 3.67 12
201897 1.833 0.936 70 -0.0327 3.70 12
200719 1.900 0.936 70 0.0339 3.77 12
201014 1.933 0.936 70 0.0673 3.80 12
200988 1.967 0.936 70 0.1006 3.83 12
200462 2.133 0.936 70 0.2673 4.00 12
201304 2.167 0.936 70 0.3006 4.03 12
200312 2.300 0.936 70 0.4339 4.17 12
200880 2.300 0.936 70 0.4339 4.17 12
200716 2.300 0.936 70 0.4339 4.17 12
200704 2.500 0.936 70 0.6339 4.37 12
200578 2.567 0.936 70 0.7006 4.43 12
control 2.600 0.936 70 0.7339 4.47 12
201854 2.600 0.936 70 0.7339 4.47 12
200477 2.600 0.936 70 0.7339 4.47 12
201078 2.633 0.936 70 0.7673 4.50 12
201861 2.733 0.936 70 0.8673 4.60 12
201154 2.733 0.936 70 0.8673 4.60 12
200983 2.833 0.936 70 0.9673 4.70 12
201267 2.867 0.936 70 1.0006 4.73 12
200555 2.867 0.936 70 1.0006 4.73 12
201844 2.933 0.936 70 1.0673 4.80 12
200438 2.950 1.146 70 0.6645 5.24 12
200797 3.467 0.936 70 1.6006 5.33 12
201914 3.733 0.936 70 1.8673 5.60 12
201315 4.200 0.936 70 2.3339 6.07 12
200976 5.500 1.146 70 3.2145 7.79 12
201088 6.500 0.936 70 4.6339 8.37 2
Confidence level used: 0.95
P value adjustment: tukey method for comparing a family of 37 estimates
significance level used: alpha = 0.1 plot(CLD(lsmeans(lm_set_3.1_DR, ~isolate), alpha=0.1), main="All pairwise comparisons for Mean Dry Root Weight", xlab = "Mean Weight (mg)", ylab = "Isolate ID")'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead.
3.1.1. - CLD - Mean Estimate Differences to Control
Compact Letter Display of estimates for Dry Root Mean weight (mg) differences between plants that were inoculated with a mucilage isolates and the non-inoculated control.
plot(CLD(lsmeans(lm_set_3.1_DR, trt.vs.ctrl~isolate,ref=37)$contrasts, alpha=0.1), main="Mean Dry Root Weight Differences of Inoculated Plants vs. Control", xlab = "Difference of Mean Estimates (mg)", ylab = "Contrasts")'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead.No information available to display confidence limits
BCW201088 has a mean dry root weight estimate with confidence intervals that do not overlap with the control, but the lower CL of 201088 is very close to the upper CL of the control estimate.
3.1.2. Dry Shoot Weight
# linear model of dry shoot data
lm_set_3.1_DS <- lm(mg ~ 1 + isolate, set_3.1_DS)
op = par(mfrow=c(1,2))
plot(lm_set_3.1_DS, which = c(2,3))not plotting observations with leverage one:
66par(op)
3.1.2. - Anova
# assess variance
anova(lm_set_3.1_DS)Analysis of Variance Table
Response: mg
Df Sum Sq Mean Sq F value Pr(>F)
isolate 36 3100.4 86.123 1.9583 0.008178 **
Residuals 70 3078.4 43.978
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 13.1.2. - LSMeans - Dunnett
lsm_s3.1.dun.DS <- summary(lsmeans(lm_set_3.1_DS, trt.vs.ctrl ~isolate, ref=37)$contrasts, infer = c(T,T))
write.csv(lsm_s3.1.dun.DS, "./lsmeans_summary_tables/lsm_s3.1.dun.DS.csv")3.1.2. - CLD - (Pairwise by isolate)
CLD(lsmeans(lm_set_3.1_DS, ~isolate), alpha=0.1)'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead. isolate lsmean SE df lower.CL upper.CL .group
200806 4.97 3.83 70 -2.670 12.6 1
200312 6.90 3.83 70 -0.736 14.5 1
201154 7.90 3.83 70 0.264 15.5 1
200880 8.43 3.83 70 0.797 16.1 1
201982 9.50 3.83 70 1.864 17.1 1
200704 10.30 3.83 70 2.664 17.9 1
200290 10.47 3.83 70 2.830 18.1 1
201938 10.53 3.83 70 2.897 18.2 1
200881 11.03 3.83 70 3.397 18.7 1
control 11.43 3.83 70 3.797 19.1 12
201902 11.53 3.83 70 3.897 19.2 12
200952 11.57 3.83 70 3.930 19.2 12
200462 11.63 3.83 70 3.997 19.3 12
200477 11.73 3.83 70 4.097 19.4 12
201014 11.93 3.83 70 4.297 19.6 12
200719 12.33 3.83 70 4.697 20.0 12
201304 12.97 3.83 70 5.330 20.6 12
200578 13.03 3.83 70 5.397 20.7 12
200983 13.50 3.83 70 5.864 21.1 12
201078 13.60 3.83 70 5.964 21.2 12
200797 14.50 3.83 70 6.864 22.1 12
201861 14.90 3.83 70 7.264 22.5 12
200438 14.90 4.69 70 5.548 24.3 12
201267 15.23 3.83 70 7.597 22.9 12
201897 15.53 3.83 70 7.897 23.2 12
201844 15.93 3.83 70 8.297 23.6 12
200588 16.20 6.63 70 2.974 29.4 12
200716 17.87 3.83 70 10.230 25.5 12
200667 19.37 3.83 70 11.730 27.0 12
201854 19.60 3.83 70 11.964 27.2 12
200988 20.80 3.83 70 13.164 28.4 12
201315 20.83 3.83 70 13.197 28.5 12
201088 20.93 3.83 70 13.297 28.6 12
200555 21.07 3.83 70 13.430 28.7 12
200525 21.17 3.83 70 13.530 28.8 12
200976 26.00 4.69 70 16.648 35.4 12
201914 31.67 3.83 70 24.030 39.3 2
Confidence level used: 0.95
P value adjustment: tukey method for comparing a family of 37 estimates
significance level used: alpha = 0.1 plot(CLD(lsmeans(lm_set_3.1_DS, ~isolate), alpha=0.1), main="All pairwise comparisons for Mean Dry Shoot Weight", xlab = "Mean Weight (mg)", ylab = "Isolate ID")'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead.
3.1.2. - CLD - Mean Estimate Differences to Control
Compact Letter Display of estimates for Dry Shoot Mean weight (mg) differences between plants that were inoculated with a mucilage isolates and the non-inoculated control.
plot(CLD(lsmeans(lm_set_3.1_DS, trt.vs.ctrl~isolate,ref=37)$contrasts, alpha=0.1), main="Mean Dry Shoot Weight Differences of Inoculated Plants vs. Control", xlab = "Difference of Mean Estimates (mg)", ylab = "Contrasts")'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead.No information available to display confidence limits
BCW201914 has a mean dry shoot weight that is nearly three times that of the control and the confidence intervals between the two do not overlap at an alpha level of 0.1.
3.1.3. Wet Root Weight
# linear model of wet root data
lm_set_3.1_WR <- lm(mg ~ 1 + isolate, set_3.1_WR)
op = par(mfrow=c(1,2))
plot(lm_set_3.1_WR, which = c(2,3))not plotting observations with leverage one:
66par(op)
3.1.3. - Anova
# assess variance
anova(lm_set_3.1_WR)Analysis of Variance Table
Response: mg
Df Sum Sq Mean Sq F value Pr(>F)
isolate 36 30271 840.87 1.7211 0.0263 *
Residuals 70 34200 488.57
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 13.1.3. - LSMeans - Dunnett
summary(lsmeans(lm_set_3.1_WR, trt.vs.ctrl ~isolate, ref=37)$contrasts, infer = c(T,T)) contrast estimate SE df lower.CL upper.CL t.ratio p.value
200290 - control -35.13 18.0 70 -93.0 22.7 -1.947 0.5926
200312 - control -17.87 18.0 70 -75.7 40.0 -0.990 0.9860
200438 - control -15.27 20.2 70 -79.9 49.4 -0.757 0.9977
200462 - control -12.40 18.0 70 -70.2 45.4 -0.687 0.9988
200477 - control -14.53 18.0 70 -72.4 43.3 -0.805 0.9965
200525 - control -26.07 18.0 70 -83.9 31.8 -1.444 0.8797
200555 - control -13.27 18.0 70 -71.1 44.6 -0.735 0.9981
200578 - control -9.10 18.0 70 -66.9 48.7 -0.504 0.9999
200588 - control -13.87 25.5 70 -95.7 67.9 -0.543 0.9998
200667 - control -14.20 18.0 70 -72.0 43.6 -0.787 0.9970
200704 - control -20.17 18.0 70 -78.0 37.7 -1.117 0.9705
200716 - control -25.53 18.0 70 -83.4 32.3 -1.415 0.8914
200719 - control -22.13 18.0 70 -80.0 35.7 -1.226 0.9493
200797 - control 1.30 18.0 70 -56.5 59.1 0.072 1.0000
200806 - control -40.30 18.0 70 -98.1 17.5 -2.233 0.4037
200880 - control -9.90 18.0 70 -67.7 47.9 -0.549 0.9998
200881 - control -23.03 18.0 70 -80.9 34.8 -1.276 0.9367
200952 - control -28.07 18.0 70 -85.9 29.8 -1.555 0.8297
200976 - control 52.28 20.2 70 -12.4 117.0 2.591 0.2129
200983 - control -2.13 18.0 70 -60.0 55.7 -0.118 1.0000
200988 - control -31.17 18.0 70 -89.0 26.7 -1.727 0.7347
201014 - control -21.37 18.0 70 -79.2 36.5 -1.184 0.9585
201078 - control -17.10 18.0 70 -74.9 40.7 -0.947 0.9895
201088 - control 27.33 18.0 70 -30.5 85.2 1.515 0.8491
201154 - control -10.63 18.0 70 -68.5 47.2 -0.589 0.9996
201267 - control -21.03 18.0 70 -78.9 36.8 -1.165 0.9621
201304 - control -26.33 18.0 70 -84.2 31.5 -1.459 0.8736
201315 - control 16.37 18.0 70 -41.5 74.2 0.907 0.9921
201844 - control -4.67 18.0 70 -62.5 53.2 -0.259 1.0000
201854 - control -5.07 18.0 70 -62.9 52.8 -0.281 1.0000
201861 - control 8.00 18.0 70 -49.8 65.8 0.443 1.0000
201897 - control -14.60 18.0 70 -72.4 43.2 -0.809 0.9964
201902 - control -35.00 18.0 70 -92.8 22.8 -1.939 0.5976
201914 - control -4.10 18.0 70 -61.9 53.7 -0.227 1.0000
201938 - control -28.93 18.0 70 -86.8 28.9 -1.603 0.8051
201982 - control -24.93 18.0 70 -82.8 32.9 -1.382 0.9037
Confidence level used: 0.95
Conf-level adjustment: dunnettx method for 36 estimates
P value adjustment: dunnettx method for 36 tests 3.1.3. - CLD - (Pairwise by isolate)
CLD(lsmeans(lm_set_3.1_WR, ~isolate), alpha=0.1)'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead. isolate lsmean SE df lower.CL upper.CL .group
200806 15.3 12.8 70 -10.19 40.7 1
200290 20.4 12.8 70 -5.02 45.9 1
201902 20.6 12.8 70 -4.89 46.0 1
200988 24.4 12.8 70 -1.05 49.9 1
201938 26.6 12.8 70 1.18 52.1 1
200952 27.5 12.8 70 2.05 53.0 1
201304 29.2 12.8 70 3.78 54.7 1
200525 29.5 12.8 70 4.05 55.0 1
200716 30.0 12.8 70 4.58 55.5 1
201982 30.6 12.8 70 5.18 56.1 1
200881 32.5 12.8 70 7.08 58.0 12
200719 33.4 12.8 70 7.98 58.9 12
201014 34.2 12.8 70 8.75 59.7 12
201267 34.5 12.8 70 9.08 60.0 12
200704 35.4 12.8 70 9.95 60.9 12
200312 37.7 12.8 70 12.25 63.2 12
201078 38.5 12.8 70 13.01 63.9 12
200438 40.3 15.6 70 9.13 71.5 12
201897 41.0 12.8 70 15.51 66.4 12
200477 41.0 12.8 70 15.58 66.5 12
200667 41.4 12.8 70 15.91 66.8 12
200588 41.7 22.1 70 -2.38 85.8 12
200555 42.3 12.8 70 16.85 67.8 12
200462 43.2 12.8 70 17.71 68.6 12
201154 44.9 12.8 70 19.48 70.4 12
200880 45.7 12.8 70 20.21 71.1 12
200578 46.5 12.8 70 21.01 71.9 12
201854 50.5 12.8 70 25.05 76.0 12
201844 50.9 12.8 70 25.45 76.4 12
201914 51.5 12.8 70 26.01 76.9 12
200983 53.4 12.8 70 27.98 78.9 12
control 55.6 12.8 70 30.11 81.0 12
200797 56.9 12.8 70 31.41 82.3 12
201861 63.6 12.8 70 38.11 89.0 12
201315 71.9 12.8 70 46.48 97.4 12
201088 82.9 12.8 70 57.45 108.4 12
200976 107.8 15.6 70 76.68 139.0 2
Confidence level used: 0.95
P value adjustment: tukey method for comparing a family of 37 estimates
significance level used: alpha = 0.1 plot(CLD(lsmeans(lm_set_3.1_WR, ~isolate), alpha=0.1), main="All pairwise comparisons for Mean Wet Root Weight", xlab = "Mean Weight (mg)", ylab = "Isolate ID")'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead.
3.1.3. - CLD - Mean Estimate Differences to Control
Compact Letter Display of estimates for Wet Root Mean weight (mg) differences between plants that were inoculated with a mucilage isolates and the non-inoculated control.
plot(CLD(lsmeans(lm_set_3.1_WR, trt.vs.ctrl~isolate,ref=37)$contrasts, alpha=0.1), main="Mean Wet Root Weight Differences of Inoculated Plants vs. Control", xlab = "Difference of Mean Estimates (mg)", ylab = "Contrasts")'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead.No information available to display confidence limits
The results for the wet root data agree with those of the dry root data.
3.1.4. Wet Shoot Weight
# linear model of wet shoot data
lm_set_3.1_WS <- lm(mg ~ 1 + isolate, set_3.1_WS)
op = par(mfrow=c(1,2))
plot(lm_set_3.1_WS, which = c(2,3))not plotting observations with leverage one:
66par(op)
3.1.4. - Anova
# assess variance
anova(lm_set_3.1_WS)Analysis of Variance Table
Response: mg
Df Sum Sq Mean Sq F value Pr(>F)
isolate 36 897234 24923 1.6178 0.04297 *
Residuals 70 1078397 15406
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 13.1.4. - LSMeans - Dunnett
lsm_s3.1.dun.WS <- summary(lsmeans(lm_set_3.1_WS, trt.vs.ctrl ~isolate, ref=37)$contrasts, infer = c(T,T))
write.csv(lsm_s3.1.dun.WS, "./lsmeans_summary_tables/lsm_s3.1.dun.WS.csv")3.1.4. - CLD
CLD(lsmeans(lm_set_3.1_WS, ~isolate), alpha=0.1)'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead. isolate lsmean SE df lower.CL upper.CL .group
200806 91.9 71.7 70 -51.0 235 1
200312 117.4 71.7 70 -25.5 260 12
201154 165.4 71.7 70 22.4 308 123
200880 165.9 71.7 70 23.0 309 123
201982 171.6 71.7 70 28.7 315 123
200881 180.3 71.7 70 37.4 323 123
201938 181.6 71.7 70 38.7 325 123
200704 185.2 71.7 70 42.3 328 123
200952 190.5 71.7 70 47.5 333 123
201304 197.4 71.7 70 54.5 340 123
200290 198.4 71.7 70 55.5 341 123
200462 200.0 71.7 70 57.0 343 123
201078 204.4 71.7 70 61.5 347 123
201902 213.3 71.7 70 70.4 356 123
control 224.2 71.7 70 81.3 367 123
200438 224.3 87.8 70 49.3 399 123
201014 235.0 71.7 70 92.1 378 123
200719 241.3 71.7 70 98.4 384 123
200983 248.3 71.7 70 105.4 391 123
200477 250.8 71.7 70 107.9 394 123
201267 256.8 71.7 70 113.8 400 123
200797 264.5 71.7 70 121.6 407 123
200578 268.8 71.7 70 125.9 412 123
200716 295.1 71.7 70 152.1 438 123
200988 304.4 71.7 70 161.4 447 123
200667 313.0 71.7 70 170.1 456 123
201844 318.4 71.7 70 175.4 461 123
201861 325.8 71.7 70 182.9 469 123
201854 331.4 71.7 70 188.5 474 123
200555 342.6 71.7 70 199.6 485 123
200588 342.6 124.1 70 95.1 590 123
201897 344.6 71.7 70 201.7 488 123
200525 349.9 71.7 70 207.0 493 123
201088 386.6 71.7 70 243.7 530 123
201315 397.7 71.7 70 254.8 541 123
201914 499.9 71.7 70 356.9 643 3
200976 523.4 87.8 70 348.3 698 23
Confidence level used: 0.95
P value adjustment: tukey method for comparing a family of 37 estimates
significance level used: alpha = 0.1 plot(CLD(lsmeans(lm_set_3.1_WS, ~isolate), alpha=0.1), main="Set 3.1: All pairwise comparisons for Mean Wet Shoot Weight", xlab = "Mean Weight (mg)", ylab = "Isolate ID")'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead.
Confidence intervals overlap between isolates with largest mean differences to the control.
Batch 3.2
# analyze set 3.2 data
str(set_3.2)'data.frame': 456 obs. of 4 variables:
$ isolate: chr "200886" "200886" "200886" "200886" ...
$ rep : int 1 1 1 1 1 1 2 2 2 2 ...
$ sample : chr "WS" "WR" "DS" "DR" ...
$ mg : num 384.8 58.7 21.8 5.5 58371.4 ...#subset by sample type
set_3.2_DR <- filter(set_3.2, sample == "DR")
set_3.2_DS <- filter(set_3.2, sample == "DS")
set_3.2_WR <- filter(set_3.2, sample == "WR")
set_3.2_WS <- filter(set_3.2, sample == "WS")Experimental Design Table
| Structure | Factor | Type | # levels |
|---|---|---|---|
| Treatment | Isolate | Qualitative | 26 |
| Design | MS Media Slants | Experimental Unit | 78 |
| Response | Plant Tissue Weight | Observational Unit | 312 |
| Response | Dry Root Weight (DR) | Variable | 78 |
| Response | Dry Shoot Weight (DS) | Variable | 78 |
| Response | Wet Root Weight (WR) | Variable | 78 |
| Response | Wet Shoot Weight (WS) | Variable | 78 |
3.2.1. Dry Root Weight
# linear model of dry root data
lm_set_3.2_DR <- lm(mg ~ 1 + isolate, set_3.2_DR)
op = par(mfrow=c(1,2))
plot(lm_set_3.2_DR, which = c(2,3))not plotting observations with leverage one:
52par(op)
3.2.1. - Anova
# assess variance
anova(lm_set_3.2_DR)Analysis of Variance Table
Response: mg
Df Sum Sq Mean Sq F value Pr(>F)
isolate 24 62.616 2.6090 1.1384 0.3424
Residuals 48 110.007 2.2918 3.2.1. - LSMeans - Dunnett
lsm_s3.2.dun.DR <- summary(lsmeans(lm_set_3.2_DR, trt.vs.ctrl ~isolate, ref=25)$contrasts, infer = c(T,T))
write.csv(lsm_s3.2.dun.DR, "./lsmeans_summary_tables/lsm_s3.2.dun.DR.csv")3.2.1. - CLD - (Pairwise by isolate)
CLD(lsmeans(lm_set_3.2_DR, ~isolate), alpha=0.1)'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead. isolate lsmean SE df lower.CL upper.CL .group
200903 1.23 0.874 48 -0.524 2.99 1
201949 1.23 0.874 48 -0.524 2.99 1
200436 1.37 0.874 48 -0.391 3.12 1
200650 1.40 0.874 48 -0.357 3.16 1
201020 1.50 1.514 48 -1.544 4.54 1
200989 1.50 0.874 48 -0.257 3.26 1
200887 2.00 0.874 48 0.243 3.76 1
201059 2.17 0.874 48 0.409 3.92 1
200544 2.30 0.874 48 0.543 4.06 1
201013 2.30 0.874 48 0.543 4.06 1
200647 2.33 0.874 48 0.576 4.09 1
201010 2.40 0.874 48 0.643 4.16 1
201444 2.63 0.874 48 0.876 4.39 1
201257 2.67 0.874 48 0.909 4.42 1
200879 2.83 0.874 48 1.076 4.59 1
201036 2.87 0.874 48 1.109 4.62 1
201445 3.13 0.874 48 1.376 4.89 1
200886 3.23 0.874 48 1.476 4.99 1
201615 3.27 0.874 48 1.509 5.02 1
200649 3.40 0.874 48 1.643 5.16 1
200499 3.50 0.874 48 1.743 5.26 1
control 3.67 0.874 48 1.909 5.42 1
200820 3.80 0.874 48 2.043 5.56 1
200776 4.30 0.874 48 2.543 6.06 1
200801 4.50 0.874 48 2.743 6.26 1
Confidence level used: 0.95
P value adjustment: tukey method for comparing a family of 25 estimates
significance level used: alpha = 0.1 plot(CLD(lsmeans(lm_set_3.2_DR, ~isolate), alpha=0.1), main="All pairwise comparisons for Mean Dry Root Weight", xlab = "Mean Weight (mg)", ylab = "Isolate ID")'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead.
3.2.1. - CLD - Mean Estimate Differences to Control
Compact Letter Display of estimates for Dry Root Mean weight (mg) differences between plants that were inoculated with a mucilage isolates and the non-inoculated control.
plot(CLD(lsmeans(lm_set_3.2_DR, trt.vs.ctrl~isolate,ref=25)$contrasts, alpha=0.1), main="Mean Dry Root Weight Differences of Inoculated Plants vs. Control", xlab = "Difference of Mean Estimates (mg)", ylab = "Contrasts")'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead.No information available to display confidence limits
There are no significantly different means estimates
3.2.2. Dry Shoot Weight
# linear model of dry shoot data
lm_set_3.2_DS <- lm(mg ~ 1 + isolate, set_3.2_DS)
op = par(mfrow=c(1,2))
plot(lm_set_3.2_DS, which = c(2,3))not plotting observations with leverage one:
52par(op)
3.2.2. - Anova
# assess variance
anova(lm_set_3.2_DS)Analysis of Variance Table
Response: mg
Df Sum Sq Mean Sq F value Pr(>F)
isolate 24 909.2 37.883 0.8117 0.7051
Residuals 48 2240.2 46.671 3.2.2. - LSMeans - Dunnett
lsm_s3.2.dun.DS <- summary(lsmeans(lm_set_3.2_DS, trt.vs.ctrl ~isolate, ref=25)$contrasts, infer = c(T,T))
write.csv(lsm_s3.2.dun.DS, "./lsmeans_summary_tables/lsm_s3.2.dun.DS.csv")3.2.2. - CLD - (Pairwise by isolate)
CLD(lsmeans(lm_set_3.2_DS, ~isolate), alpha=0.1)'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead. isolate lsmean SE df lower.CL upper.CL .group
201036 6.27 3.94 48 -1.664 14.2 1
200903 7.17 3.94 48 -0.764 15.1 1
200647 7.30 3.94 48 -0.630 15.2 1
201257 8.87 3.94 48 0.936 16.8 1
200436 9.77 3.94 48 1.836 17.7 1
201615 10.37 3.94 48 2.436 18.3 1
200820 10.50 3.94 48 2.570 18.4 1
200801 10.73 3.94 48 2.803 18.7 1
201013 11.07 3.94 48 3.136 19.0 1
200887 11.53 3.94 48 3.603 19.5 1
200499 12.20 3.94 48 4.270 20.1 1
201059 12.40 3.94 48 4.470 20.3 1
200886 12.47 3.94 48 4.536 20.4 1
200650 12.60 3.94 48 4.670 20.5 1
201949 12.60 3.94 48 4.670 20.5 1
201445 12.97 3.94 48 5.036 20.9 1
200989 13.03 3.94 48 5.103 21.0 1
201444 15.10 3.94 48 7.170 23.0 1
200544 15.23 3.94 48 7.303 23.2 1
201020 15.50 6.83 48 1.764 29.2 1
200649 16.17 3.94 48 8.236 24.1 1
201010 16.27 3.94 48 8.336 24.2 1
200879 17.63 3.94 48 9.703 25.6 1
control 18.73 3.94 48 10.803 26.7 1
200776 20.43 3.94 48 12.503 28.4 1
Confidence level used: 0.95
P value adjustment: tukey method for comparing a family of 25 estimates
significance level used: alpha = 0.1 plot(CLD(lsmeans(lm_set_3.2_DS, ~isolate), alpha=0.1), main="All pairwise comparisons for Mean Dry Shoot Weight", xlab = "Mean Weight (mg)", ylab = "Isolate ID")'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead.
3.2.2. - CLD - Mean Estimate Differences to Control
Compact Letter Display of estimates for Dry Shoot Mean weight (mg) differences between plants that were inoculated with a mucilage isolates and the non-inoculated control.
plot(CLD(lsmeans(lm_set_3.2_DS, trt.vs.ctrl~isolate,ref=25)$contrasts, alpha=0.1), main="Mean Dry Shoot Weight Differences of Inoculated Plants vs. Control", xlab = "Difference of Mean Estimates (mg)", ylab = "Contrasts")'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead.No information available to display confidence limits
There are no significantly different means estimates for Dry Shoot weight in Batch 3.2.
3.2.3. Wet Root Data
# linear model of wet root data
lm_set_3.2_WR <- lm(mg ~ 1 + isolate, set_3.2_WR)
op = par(mfrow=c(1,2))
plot(lm_set_3.2_WR, which = c(2,3))not plotting observations with leverage one:
52par(op)
3.2.3. - Anova
# assess variance
anova(lm_set_3.2_WR)Analysis of Variance Table
Response: mg
Df Sum Sq Mean Sq F value Pr(>F)
isolate 24 12877 536.56 1.3548 0.1827
Residuals 48 19011 396.06 3.2.3. - LSMeans - Dunnett
lsm_s3.2.dun.WR <- summary(lsmeans(lm_set_3.2_WR, trt.vs.ctrl ~isolate, ref=25)$contrasts, infer = c(T,T))
write.csv(lsm_s3.2.dun.WR, "./lsmeans_summary_tables/lsm_s3.2.dun.WR.csv")3.2.3. - CLD - (Pairwise by isolate)
CLD(lsmeans(lm_set_3.2_WR, ~isolate), alpha=0.1)'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead. isolate lsmean SE df lower.CL upper.CL .group
200903 16.1 11.5 48 -6.97 39.2 1
200436 17.5 11.5 48 -5.57 40.6 1
200650 18.5 11.5 48 -4.60 41.6 1
200989 18.8 11.5 48 -4.30 41.9 1
201949 19.7 11.5 48 -3.40 42.8 1
201020 20.0 19.9 48 -20.01 60.0 1
200887 24.2 11.5 48 1.13 47.3 1
201059 25.2 11.5 48 2.10 48.3 1
200647 28.8 11.5 48 5.66 51.9 1
201036 29.3 11.5 48 6.23 52.4 1
201013 29.8 11.5 48 6.70 52.9 1
201010 31.4 11.5 48 8.26 54.5 1
200544 32.0 11.5 48 8.86 55.1 1
201444 35.1 11.5 48 12.03 58.2 1
201445 35.4 11.5 48 12.26 58.5 1
200879 35.6 11.5 48 12.50 58.7 1
200886 37.1 11.5 48 14.00 60.2 1
200820 40.8 11.5 48 17.70 63.9 1
201257 41.0 11.5 48 17.86 64.1 1
201615 41.3 11.5 48 18.23 64.4 1
200499 47.3 11.5 48 24.16 70.4 1
200649 48.6 11.5 48 25.46 71.7 1
200801 58.0 11.5 48 34.93 81.1 1
200776 59.0 11.5 48 35.93 82.1 1
control 65.0 11.5 48 41.93 88.1 1
Confidence level used: 0.95
P value adjustment: tukey method for comparing a family of 25 estimates
significance level used: alpha = 0.1 plot(CLD(lsmeans(lm_set_3.2_WR, ~isolate), alpha=0.1), main="All pairwise comparisons for Mean Wet Root Weight", xlab = "Mean Weight (mg)", ylab = "Isolate ID")'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead.
3.2.3. - CLD - Mean Estimate Differences to Control
Compact Letter Display of estimates for Wet Root Mean weight (mg) differences between plants that were inoculated with a mucilage isolates and the non-inoculated control.
plot(CLD(lsmeans(lm_set_3.2_WR, trt.vs.ctrl~isolate,ref=25)$contrasts, alpha=0.1), main="Mean Wet Root Weight Differences of Inoculated Plants vs. Control", xlab = "Difference of Mean Estimates (mg)", ylab = "Contrasts")'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead.No information available to display confidence limits
There are no significantly different means estimates for wet root weight in batch 3.2.
3.2.4. Wet Shoot Data
# linear model of wet shoot data
lm_set_3.2_WS <- lm(mg ~ 1 + isolate, set_3.2_WS)
op = par(mfrow=c(1,2))
plot(lm_set_3.2_WS, which = c(2,3))not plotting observations with leverage one:
52par(op)
3.2.4. - Anova
# assess variance
anova(lm_set_3.2_WS)Analysis of Variance Table
Response: mg
Df Sum Sq Mean Sq F value Pr(>F)
isolate 24 359228 14968 1.0287 0.4531
Residuals 47 683848 14550 3.2.4. - LSMeans - Dunnett
lsm_s3.2.dun.WS <- summary(lsmeans(lm_set_3.2_WS, trt.vs.ctrl ~isolate, ref=25)$contrasts, infer = c(T,T))
write.csv(lsm_s3.2.dun.WS, "./lsmeans_summary_tables/lsm_s3.2.dun.WS.csv")3.2.4. - CLD - (Pairwise by isolate)
CLD(lsmeans(lm_set_3.2_WS, ~isolate), alpha=0.1)'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead. isolate lsmean SE df lower.CL upper.CL .group
201036 100 69.6 47 -39.9 240 1
200903 106 69.6 47 -33.9 246 1
200647 121 69.6 47 -19.5 261 1
201615 153 69.6 47 12.9 293 1
200820 160 69.6 47 19.5 300 1
201257 168 69.6 47 28.0 308 1
200436 173 69.6 47 33.1 313 1
200887 179 69.6 47 39.3 320 1
200801 181 69.6 47 41.4 322 1
201020 192 120.6 47 -51.2 434 1
201059 192 69.6 47 52.3 333 1
200499 194 69.6 47 54.2 334 1
200989 205 69.6 47 64.9 345 1
201013 205 69.6 47 65.3 345 1
200544 206 69.6 47 65.7 346 1
200650 208 69.6 47 67.4 348 1
201949 213 69.6 47 72.7 353 1
200886 215 69.6 47 75.1 355 1
201445 252 69.6 47 111.9 392 1
201444 260 69.6 47 119.4 400 1
200879 289 69.6 47 149.0 429 1
201010 294 69.6 47 153.5 434 1
200649 327 85.3 47 155.8 499 1
200776 350 69.6 47 209.4 490 1
control 387 69.6 47 247.4 528 1
Confidence level used: 0.95
P value adjustment: tukey method for comparing a family of 25 estimates
significance level used: alpha = 0.1 plot(CLD(lsmeans(lm_set_3.2_WS, ~isolate), alpha=0.1), main="All pairwise comparisons for Mean Wet Shoot Weight", xlab = "Mean Weight (mg)", ylab = "Isolate ID")'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead.
3.2.4. - CLD - Mean Estimate Differences to Control
Compact Letter Display of estimates for Wet Shoot Mean weight (mg) differences between plants that were inoculated with a mucilage isolates and the non-inoculated control.
plot(CLD(lsmeans(lm_set_3.2_WS, trt.vs.ctrl~isolate,ref=25)$contrasts, alpha=0.1), main="Mean Wet Shoot Weight Differences of Inoculated Plants vs. Control", xlab = "Difference of Mean Estimates (mg)", ylab = "Contrasts")'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead.No information available to display confidence limits
There are no significantly different means estimates. Control is the highest mean, all inoculated platlets exhibited lower mean wet shoot weight.
Batch 3.3
# analyze set 3.3 data
str(set_3.3)'data.frame': 672 obs. of 4 variables:
$ isolate: chr "200798" "200798" "200798" "200798" ...
$ rep : int 1 1 1 1 1 1 2 2 2 2 ...
$ sample : chr "WS" "WR" "DS" "DR" ...
$ mg : num 68.3 11.6 5.8 0.4 57506.5 ...#subset by sample type
set_3.3_DR <- filter(set_3.3, sample == "DR")
set_3.3_DS <- filter(set_3.3, sample == "DS")
set_3.3_WR <- filter(set_3.3, sample == "WR")
set_3.3_WS <- filter(set_3.3, sample == "WS")Experimental Design Table
| Structure | Factor | Type | # levels |
|---|---|---|---|
| Treatment | Isolate | Qualitative | 38 |
| Design | MS Media Slants | Experimental Unit | 114 |
| Response | Plant Tissue Weight | Observational Unit | 456 |
| Response | Dry Root Weight (DR) | Variable | 114 |
| Response | Dry Shoot Weight (DS) | Variable | 114 |
| Response | Wet Root Weight (WR) | Variable | 114 |
| Response | Wet Shoot Weight (WS) | Variable | 114 |
3.3.1. Dry Root Weight
# linear model of dry root data
lm_set_3.3_DR <- lm(mg ~ 1 + isolate, set_3.3_DR)
op = par(mfrow=c(1,2))
plot(lm_set_3.3_DR, which = c(2,3))
par(op)
3.3.1. - Anova
# assess variance
anova(lm_set_3.3_DR)Analysis of Variance Table
Response: mg
Df Sum Sq Mean Sq F value Pr(>F)
isolate 36 117.722 3.2701 2.4963 0.0004876 ***
Residuals 72 94.318 1.3100
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 13.3.1. - LSMeans - Dunnett
lsm_s3.3.dun.DR <- summary(lsmeans(lm_set_3.3_DR, trt.vs.ctrl ~isolate, ref=37)$contrasts, infer = c(T,T))
write.csv(lsm_s3.3.dun.DR, "./lsmeans_summary_tables/lsm_s3.3.dun.DR.csv")3.3.1. - CLD - (Pairwise by isolate)
CLD(lsmeans(lm_set_3.3_DR, ~isolate), alpha=0.1)'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead. isolate lsmean SE df lower.CL upper.CL .group
200815 0.733 0.661 72 -0.584 2.05 1
201937 1.233 0.661 72 -0.084 2.55 1
200574 1.233 0.661 72 -0.084 2.55 1
201721 1.500 0.661 72 0.183 2.82 1
201839 1.567 0.661 72 0.249 2.88 1
201828 1.567 0.661 72 0.249 2.88 1
201614 1.600 0.661 72 0.283 2.92 1
200798 1.633 0.661 72 0.316 2.95 1
202005 1.633 0.661 72 0.316 2.95 1
200909 1.633 0.661 72 0.316 2.95 1
201975 1.700 0.661 72 0.383 3.02 1
200521 1.767 0.661 72 0.449 3.08 1
201726 1.900 0.661 72 0.583 3.22 1
201811 1.933 0.661 72 0.616 3.25 1
201649 1.933 0.661 72 0.616 3.25 1
201021 2.133 0.661 72 0.816 3.45 1
200912 2.167 0.661 72 0.849 3.48 1
201081 2.400 0.661 72 1.083 3.72 1
200557 2.400 0.661 72 1.083 3.72 1
201071 2.533 0.661 72 1.216 3.85 1
200272 2.633 0.661 72 1.316 3.95 1
200281 2.650 0.809 72 1.037 4.26 1
200855 2.733 0.661 72 1.416 4.05 1
200442 2.733 0.661 72 1.416 4.05 1
201648 2.800 0.661 72 1.483 4.12 1
200641 2.800 0.661 72 1.483 4.12 1
200818 3.067 0.661 72 1.749 4.38 1
200656 3.100 0.661 72 1.783 4.42 1
200945 3.100 0.661 72 1.783 4.42 1
200497 3.200 0.809 72 1.587 4.81 12
200520 3.233 0.661 72 1.916 4.55 12
201450 3.233 0.661 72 1.916 4.55 12
201008 3.267 0.661 72 1.949 4.58 12
201441 3.433 0.661 72 2.116 4.75 12
200434 3.700 0.661 72 2.383 5.02 12
201903 3.800 0.661 72 2.483 5.12 12
control 6.667 0.661 72 5.349 7.98 2
Confidence level used: 0.95
P value adjustment: tukey method for comparing a family of 37 estimates
significance level used: alpha = 0.1 plot(CLD(lsmeans(lm_set_3.3_DR, ~isolate), alpha=0.1), main="All pairwise comparisons for Mean Dry Root Weight", xlab = "Mean Weight (mg)", ylab = "Isolate ID")'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead.
3.3.1. - CLD - Mean Estimate Differences to Control
Compact Letter Display of estimates for Dry Root Mean weight (mg) differences between plants that were inoculated with a mucilage isolates and the non-inoculated control.
plot(CLD(lsmeans(lm_set_3.3_DR, trt.vs.ctrl~isolate,ref=37)$contrasts, alpha=0.1), main="Mean Dry Root Weight Differences of Inoculated Plants vs. Control", xlab = "Difference of Mean Estimates (mg)", ylab = "Contrasts")'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead.No information available to display confidence limits
Here, the control mean is much higher than most of the inoculated samples.
3.3.2. Dry Shoot Weight
# linear model of dry shoot data
lm_set_3.3_DS <- lm(mg ~ 1 + isolate, set_3.3_DS)
op = par(mfrow=c(1,2))
plot(lm_set_3.3_DS, which = c(2,3))
par(op)
3.3.2. - Anova
# assess variance
anova(lm_set_3.3_DS)Analysis of Variance Table
Response: mg
Df Sum Sq Mean Sq F value Pr(>F)
isolate 36 2315.0 64.304 1.3704 0.128
Residuals 72 3378.4 46.923 3.3.2. - LSMeans - Dunnett
lsm_s3.3.dun.DS <- summary(lsmeans(lm_set_3.3_DS, trt.vs.ctrl ~isolate, ref=37)$contrasts, infer = c(T,T))
write.csv(lsm_s3.3.dun.DS, "./lsmeans_summary_tables/lsm_s3.3.dun.DS.csv")3.3.2. - CLD - (Pairwise by isolate)
CLD(lsmeans(lm_set_3.3_DS, ~isolate), alpha=0.1)'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead. isolate lsmean SE df lower.CL upper.CL .group
201937 7.37 3.95 72 -0.5172 15.3 1
201726 7.87 3.95 72 -0.0172 15.8 1
200815 8.37 3.95 72 0.4828 16.3 1
200521 9.73 3.95 72 1.8495 17.6 1
201614 10.33 3.95 72 2.4495 18.2 1
200909 11.47 3.95 72 3.5828 19.4 1
201839 12.20 3.95 72 4.3161 20.1 1
201008 12.60 3.95 72 4.7161 20.5 12
200798 12.90 3.95 72 5.0161 20.8 12
201811 12.93 3.95 72 5.0495 20.8 12
200497 13.15 4.84 72 3.4943 22.8 12
201828 13.77 3.95 72 5.8828 21.7 12
200818 13.80 3.95 72 5.9161 21.7 12
201721 13.93 3.95 72 6.0495 21.8 12
200855 13.97 3.95 72 6.0828 21.9 12
200557 13.97 3.95 72 6.0828 21.9 12
202005 14.27 3.95 72 6.3828 22.2 12
201081 14.33 3.95 72 6.4495 22.2 12
201975 14.40 3.95 72 6.5161 22.3 12
201021 14.47 3.95 72 6.5828 22.4 12
201071 14.57 3.95 72 6.6828 22.5 12
200442 15.60 3.95 72 7.7161 23.5 12
201649 15.83 3.95 72 7.9495 23.7 12
200574 16.00 3.95 72 8.1161 23.9 12
200945 16.20 3.95 72 8.3161 24.1 12
200281 17.30 4.84 72 7.6443 27.0 12
201450 17.37 3.95 72 9.4828 25.3 12
200641 17.67 3.95 72 9.7828 25.6 12
200434 17.67 3.95 72 9.7828 25.6 12
201441 17.70 3.95 72 9.8161 25.6 12
201648 17.93 3.95 72 10.0495 25.8 12
200912 18.07 3.95 72 10.1828 26.0 12
200272 18.53 3.95 72 10.6495 26.4 12
200656 19.13 3.95 72 11.2495 27.0 12
200520 20.10 3.95 72 12.2161 28.0 12
201903 23.90 3.95 72 16.0161 31.8 12
control 33.40 3.95 72 25.5161 41.3 2
Confidence level used: 0.95
P value adjustment: tukey method for comparing a family of 37 estimates
significance level used: alpha = 0.1 plot(CLD(lsmeans(lm_set_3.3_DS, ~isolate), alpha=0.1), main="All pairwise comparisons for Mean Dry Shoot Weight", xlab = "Mean Weight (mg)", ylab = "Isolate ID")'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead.
3.3.2. - CLD - Mean Estimate Differences to Control
Compact Letter Display of estimates for Dry Shoot Mean weight (mg) differences between plants that were inoculated with a mucilage isolates and the non-inoculated control.
plot(CLD(lsmeans(lm_set_3.3_DS, trt.vs.ctrl~isolate,ref=37)$contrasts, alpha=0.1), main="Mean Dry Shoot Weight Differences of Inoculated Plants vs. Control", xlab = "Difference of Mean Estimates (mg)", ylab = "Contrasts")'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead.No information available to display confidence limits
Again, we have negative differences in mean weight relative to the control.
3.3.3. Wet Root Weight
# linear model of wet root data
lm_set_3.3_WR <- lm(mg ~ 1 + isolate, set_3.3_WR)
op = par(mfrow=c(1,2))
plot(lm_set_3.3_WR, which = c(2,3))
par(op)
3.3.3. - Anova
# assess variance
anova(lm_set_3.3_WR)Analysis of Variance Table
Response: mg
Df Sum Sq Mean Sq F value Pr(>F)
isolate 36 41260 1146.11 3.5581 2.3e-06 ***
Residuals 72 23192 322.11
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 13.3.3. - LSMeans - Dunnett
lsm_s3.3.dun.WR <- summary(lsmeans(lm_set_3.3_WR, trt.vs.ctrl ~isolate, ref=37)$contrasts, infer = c(T,T))
write.csv(lsm_s3.3.dun.WR, "./lsmeans_summary_tables/lsm_s3.3.dun.WR.csv")3.3.3. - CLD - (Pairwise by isolate)
CLD(lsmeans(lm_set_3.3_WR, ~isolate), alpha=0.1)'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead. isolate lsmean SE df lower.CL upper.CL .group
200815 9.07 10.4 72 -11.590 29.7 1
201937 14.80 10.4 72 -5.856 35.5 1
200574 17.30 10.4 72 -3.356 38.0 1
201828 19.60 10.4 72 -1.056 40.3 1
200909 20.83 10.4 72 0.177 41.5 1
201721 21.00 10.4 72 0.344 41.7 1
202005 21.33 10.4 72 0.677 42.0 1
201975 21.53 10.4 72 0.877 42.2 1
201021 21.60 10.4 72 0.944 42.3 1
201839 22.20 10.4 72 1.544 42.9 1
201614 22.43 10.4 72 1.777 43.1 1
200798 23.57 10.4 72 2.910 44.2 1
200521 23.93 10.4 72 3.277 44.6 1
201649 24.80 10.4 72 4.144 45.5 1
201811 26.20 10.4 72 5.544 46.9 1
200557 28.30 10.4 72 7.644 49.0 1
201081 29.00 10.4 72 8.344 49.7 1
200912 29.07 10.4 72 8.410 49.7 1
201726 32.23 10.4 72 11.577 52.9 1
201071 36.00 10.4 72 15.344 56.7 1
201648 36.13 10.4 72 15.477 56.8 1
200442 36.17 10.4 72 15.510 56.8 1
200945 36.67 10.4 72 16.010 57.3 1
201008 37.60 10.4 72 16.944 58.3 1
201450 37.83 10.4 72 17.177 58.5 1
200855 38.00 10.4 72 17.344 58.7 1
200272 38.03 10.4 72 17.377 58.7 1
200818 39.53 10.4 72 18.877 60.2 1
200641 41.97 10.4 72 21.310 62.6 1
201903 43.50 10.4 72 22.844 64.2 1
200520 44.20 10.4 72 23.544 64.9 1
200281 45.60 12.7 72 20.301 70.9 1
200497 47.50 12.7 72 22.201 72.8 1
201441 47.53 10.4 72 26.877 68.2 1
200656 51.53 10.4 72 30.877 72.2 1
200434 52.87 10.4 72 32.210 73.5 1
control 130.57 10.4 72 109.910 151.2 2
Confidence level used: 0.95
P value adjustment: tukey method for comparing a family of 37 estimates
significance level used: alpha = 0.1 plot(CLD(lsmeans(lm_set_3.3_WR, ~isolate), alpha=0.1), main="All pairwise comparisons for Mean Wet Root Weight", xlab = "Mean Weight (mg)", ylab = "Isolate ID")'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead.
3.3.3. - CLD - Mean Estimate Differences to Control
Compact Letter Display of estimates for Wet Root Mean weight (mg) differences between plants that were inoculated with a mucilage isolates and the non-inoculated control.
plot(CLD(lsmeans(lm_set_3.3_WR, trt.vs.ctrl~isolate,ref=37)$contrasts, alpha=0.1), main="Mean Wet Root Weight Differences of Inoculated Plants vs. Control", xlab = "Difference of Mean Estimates (mg)", ylab = "Contrasts")'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead.No information available to display confidence limits
Effect of data distribution, or control is bad, or the isolates hinder growth?
3.3.4. Wet Shoot Weight
# linear model of wet shoot data
lm_set_3.3_WS <- lm(mg ~ 1 + isolate, set_3.3_WS)
op = par(mfrow=c(1,2))
plot(lm_set_3.3_WS, which = c(2,3))
par(op)
3.3.4. - Anova
# assess variance
anova(lm_set_3.3_WS)Analysis of Variance Table
Response: mg
Df Sum Sq Mean Sq F value Pr(>F)
isolate 36 965186 26811 1.6313 0.03937 *
Residuals 72 1183354 16436
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 13.3.4. - LSMeans - Dunnett
lsm_s3.3.dun.WS <- summary(lsmeans(lm_set_3.3_WS, trt.vs.ctrl ~isolate, ref=37)$contrasts, infer = c(T,T))
write.csv(lsm_s3.3.dun.WS, "./lsmeans_summary_tables/lsm_s3.3.dun.WS.csv")3.3.4. - CLD - (Pairwise by isolate)
CLD(lsmeans(lm_set_3.3_WS, ~isolate), alpha=0.1)'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead. isolate lsmean SE df lower.CL upper.CL .group
201937 95 74.0 72 -52.52 243 1
200815 111 74.0 72 -36.72 258 1
201726 144 74.0 72 -3.32 292 1
200798 146 74.0 72 -1.95 293 1
200521 160 74.0 72 12.05 307 1
201614 162 74.0 72 14.05 309 1
200909 169 74.0 72 21.58 317 1
201008 181 74.0 72 33.12 328 1
200557 190 74.0 72 41.98 337 1
201828 193 74.0 72 45.18 340 1
201811 198 74.0 72 50.62 346 1
201839 200 74.0 72 52.18 347 1
202005 205 74.0 72 57.55 353 1
201021 214 74.0 72 66.12 361 1
201721 214 74.0 72 66.22 361 1
201975 218 74.0 72 70.32 365 1
200855 219 74.0 72 71.38 366 1
201081 226 74.0 72 78.52 374 1
201071 228 74.0 72 80.68 376 1
200442 234 74.0 72 86.18 381 1
200818 234 74.0 72 86.22 381 1
201649 234 74.0 72 86.72 382 1
201450 237 74.0 72 89.22 384 1
200497 237 90.7 72 56.44 418 12
200574 262 74.0 72 114.25 409 12
200641 267 74.0 72 119.55 415 12
200945 273 74.0 72 125.55 421 12
200912 282 74.0 72 134.25 429 12
200656 296 74.0 72 148.08 443 12
200520 298 74.0 72 150.95 446 12
201441 306 74.0 72 158.18 453 12
200281 307 90.7 72 126.59 488 12
201648 319 74.0 72 171.68 467 12
200434 329 74.0 72 181.45 477 12
200272 338 74.0 72 190.92 486 12
201903 376 74.0 72 228.68 524 12
control 655 74.0 72 507.62 803 2
Confidence level used: 0.95
P value adjustment: tukey method for comparing a family of 37 estimates
significance level used: alpha = 0.1 plot(CLD(lsmeans(lm_set_3.3_WS, ~isolate), alpha=0.1), main="All pairwise comparisons for Mean Wet Shoot Weight", xlab = "Mean Weight (mg)", ylab = "Isolate ID")'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead.
3.3.4. - CLD - Mean Estimate Differences to Control
Compact Letter Display of estimates for Wet Shoot Mean weight (mg) differences between plants that were inoculated with a mucilage isolates and the non-inoculated control.
plot(CLD(lsmeans(lm_set_3.3_WS, trt.vs.ctrl~isolate,ref=37)$contrasts, alpha=0.1), main="Mean Wet Shoot Weight Differences of Inoculated Plants vs. Control", xlab = "Difference of Mean Estimates (mg)", ylab = "Contrasts")'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead.No information available to display confidence limits
The control mean weight is much higher than inoculated plant mean weights for wet shoot.
Batch 3.4
# analyze set 3.4 data
str(set_3.4)'data.frame': 672 obs. of 4 variables:
$ isolate: chr "200278" "200278" "200278" "200278" ...
$ rep : int 1 1 1 1 1 1 2 2 2 2 ...
$ sample : chr "WS" "WR" "DS" "DR" ...
$ mg : num 333.2 49 22.9 3.4 59602.5 ...#subset by sample type
set_3.4_DR <- filter(set_3.4, sample == "DR")
set_3.4_DS <- filter(set_3.4, sample == "DS")
set_3.4_WR <- filter(set_3.4, sample == "WR")
set_3.4_WS <- filter(set_3.4, sample == "WS")Experimental Design Table
| Structure | Factor | Type | # levels |
|---|---|---|---|
| Treatment | Isolate | Qualitative | 38 |
| Design | MS Media Slants | Experimental Unit | 114 |
| Response | Plant Tissue Weight | Observational Unit | 456 |
| Response | Dry Root Weight (DR) | Variable | 114 |
| Response | Dry Shoot Weight (DS) | Variable | 114 |
| Response | Wet Root Weight (WR) | Variable | 114 |
| Response | Wet Shoot Weight (WS) | Variable | 114 |
3.4.1. Dry Root Weight
# linear model of dry root data
lm_set_3.4_DR <- lm(mg ~ 1 + isolate, set_3.4_DR)
op = par(mfrow=c(1,2))
plot(lm_set_3.4_DR, which = c(2,3))
par(op)
3.4.1. - Anova
# assess variance
anova(lm_set_3.4_DR)Analysis of Variance Table
Response: mg
Df Sum Sq Mean Sq F value Pr(>F)
isolate 35 88.451 2.5272 2.0434 0.005613 **
Residuals 70 86.572 1.2367
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 13.4.1. - LSMeans - Dunnett
lsm_s3.4.dun.DR <- summary(lsmeans(lm_set_3.4_DR, trt.vs.ctrl ~isolate, ref=36)$contrasts, infer = c(T,T))
write.csv(lsm_s3.4.dun.DR, "./lsmeans_summary_tables/lsm_s3.4.dun.DR.csv")3.4.1. - CLD - (Pairwise by isolate)
CLD(lsmeans(lm_set_3.4_DR, ~isolate), alpha=0.1)'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead. isolate lsmean SE df lower.CL upper.CL .group
200651 1.10 0.642 70 -0.181 2.38 1
201995 1.10 0.642 70 -0.181 2.38 1
201070 1.20 0.786 70 -0.368 2.77 1
201107 1.53 0.642 70 0.253 2.81 1
201007 1.53 0.642 70 0.253 2.81 1
200891 1.57 0.642 70 0.286 2.85 1
200308 1.67 0.642 70 0.386 2.95 1
201653 1.70 0.642 70 0.419 2.98 1
201054 1.73 0.642 70 0.453 3.01 1
200542 1.77 0.642 70 0.486 3.05 1
201620 1.80 0.642 70 0.519 3.08 1
201082 1.80 0.642 70 0.519 3.08 1
200892 1.90 0.642 70 0.619 3.18 1
200790 1.97 0.642 70 0.686 3.25 1
200276 2.23 0.642 70 0.953 3.51 12
201044 2.40 0.642 70 1.119 3.68 12
200509 2.40 0.642 70 1.119 3.68 12
201248 2.47 0.642 70 1.186 3.75 12
201842 2.50 0.642 70 1.219 3.78 12
200465 2.53 0.642 70 1.253 3.81 12
200559 2.53 0.642 70 1.253 3.81 12
200993 2.60 0.642 70 1.319 3.88 12
200271 2.63 0.642 70 1.353 3.91 12
200646 2.65 0.786 70 1.082 4.22 12
200278 2.70 0.642 70 1.419 3.98 12
200437 2.70 0.642 70 1.419 3.98 12
201028 2.77 0.642 70 1.486 4.05 12
201293 2.77 0.642 70 1.486 4.05 12
200785 2.80 0.642 70 1.519 4.08 12
201461 3.00 0.642 70 1.719 4.28 12
200317 3.10 0.642 70 1.819 4.38 12
200944 3.43 0.642 70 2.153 4.71 12
200464 3.57 0.642 70 2.286 4.85 12
control 4.07 0.642 70 2.786 5.35 12
200791 4.33 0.642 70 3.053 5.61 12
200432 5.47 0.642 70 4.186 6.75 2
Confidence level used: 0.95
P value adjustment: tukey method for comparing a family of 36 estimates
significance level used: alpha = 0.1 plot(CLD(lsmeans(lm_set_3.4_DR, ~isolate), alpha=0.1), main="All pairwise comparisons for Mean Dry Root Weight", xlab = "Mean Weight (mg)", ylab = "Isolate ID")'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead.
3.4.1. - CLD - Mean Estimate Differences to Control
Compact Letter Display of estimates for Dry Root Mean weight (mg) differences between plants that were inoculated with a mucilage isolates and the non-inoculated control.
plot(CLD(lsmeans(lm_set_3.4_DR, trt.vs.ctrl~isolate,ref=36)$contrasts, alpha=0.1), main="Mean Dry Root Weight Differences of Inoculated Plants vs. Control", xlab = "Difference of Mean Estimates (mg)", ylab = "Contrasts")'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead.No information available to display confidence limits
There are not significant positive differences between mean estimates for inoculated plants and the control plants with respect to dry root weight measurements in batch 3.4.
3.4.2. Dry Shoot Weight
# linear model of dry shoot data
lm_set_3.4_DS <- lm(mg ~ 1 + isolate, set_3.4_DS)
op = par(mfrow=c(1,2))
plot(lm_set_3.4_DS, which = c(2,3))
par(op)
3.4.2. - Anova
# assess variance
anova(lm_set_3.4_DS)Analysis of Variance Table
Response: mg
Df Sum Sq Mean Sq F value Pr(>F)
isolate 35 2611.0 74.600 1.8568 0.01412 *
Residuals 70 2812.3 40.176
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 13.4.2. - LSMeans - Dunnett
lsm_s3.4.dun.DS <- summary(lsmeans(lm_set_3.4_DS, trt.vs.ctrl ~isolate, ref=36)$contrasts, infer = c(T,T))
write.csv(lsm_s3.4.dun.DS, "./lsmeans_summary_tables/lsm_s3.4.dun.DS.csv")3.4.2. - CLD - (Pairwise by isolate)
CLD(lsmeans(lm_set_3.4_DS, ~isolate), alpha=0.1)'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead. isolate lsmean SE df lower.CL upper.CL .group
201070 5.70 4.48 70 -3.239 14.6 1
200542 7.60 3.66 70 0.301 14.9 1
201995 9.03 3.66 70 1.735 16.3 12
201007 9.33 3.66 70 2.035 16.6 12
200790 10.37 3.66 70 3.068 17.7 12
200651 10.47 3.66 70 3.168 17.8 12
201107 10.77 3.66 70 3.468 18.1 12
200892 12.10 3.66 70 4.801 19.4 12
200276 12.53 3.66 70 5.235 19.8 12
200891 12.57 3.66 70 5.268 19.9 12
201082 13.23 3.66 70 5.935 20.5 12
201653 13.67 3.66 70 6.368 21.0 12
200308 14.00 3.66 70 6.701 21.3 12
201028 15.07 3.66 70 7.768 22.4 12
201054 15.30 3.66 70 8.001 22.6 12
200465 15.37 3.66 70 8.068 22.7 12
200278 15.80 3.66 70 8.501 23.1 12
200317 16.20 3.66 70 8.901 23.5 12
201842 16.23 3.66 70 8.935 23.5 12
200271 16.30 3.66 70 9.001 23.6 12
201248 16.50 3.66 70 9.201 23.8 12
control 18.30 3.66 70 11.001 25.6 12
201293 18.57 3.66 70 11.268 25.9 12
200646 18.60 4.48 70 9.661 27.5 12
200785 18.67 3.66 70 11.368 26.0 12
201044 18.67 3.66 70 11.368 26.0 12
200559 18.80 3.66 70 11.501 26.1 12
201461 20.23 3.66 70 12.935 27.5 12
200993 21.20 3.66 70 13.901 28.5 12
200437 21.27 3.66 70 13.968 28.6 12
201620 22.47 3.66 70 15.168 29.8 12
200944 22.47 3.66 70 15.168 29.8 12
200509 22.73 3.66 70 15.435 30.0 12
200464 22.77 3.66 70 15.468 30.1 12
200432 24.67 3.66 70 17.368 32.0 12
200791 27.43 3.66 70 20.135 34.7 2
Confidence level used: 0.95
P value adjustment: tukey method for comparing a family of 36 estimates
significance level used: alpha = 0.1 plot(CLD(lsmeans(lm_set_3.4_DS, ~isolate), alpha=0.1), main="All pairwise comparisons for Mean Dry Shoot Weight", xlab = "Mean Weight (mg)", ylab = "Isolate ID")'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead.
3.4.2. - CLD - Mean Estimate Differences to Control
Compact Letter Display of estimates for Dry Shoot Mean weight (mg) differences between plants that were inoculated with a mucilage isolates and the non-inoculated control.
plot(CLD(lsmeans(lm_set_3.4_DS, trt.vs.ctrl~isolate,ref=36)$contrasts, alpha=0.1), main="Mean Dry Shoot Weight Differences of Inoculated Plants vs. Control", xlab = "Difference of Mean Estimates (mg)", ylab = "Contrasts")'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead.No information available to display confidence limits
All isolates have mean estimates for dry shoot mass with confidence intervals that overlap with those of the control group.
3.4.3. Wet Root Weight
# linear model of wet root data
lm_set_3.4_WR <- lm(mg ~ 1 + isolate, set_3.4_WR)
op = par(mfrow=c(1,2))
plot(lm_set_3.4_WR, which = c(2,3))
par(op)
3.4.3. - Anova
# assess variance
anova(lm_set_3.4_WR)Analysis of Variance Table
Response: mg
Df Sum Sq Mean Sq F value Pr(>F)
isolate 35 23914 683.26 3.6402 2.118e-06 ***
Residuals 70 13139 187.70
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 13.4.3. - LSMeans - Dunnett
lsm_s3.4.dun.WR <- summary(lsmeans(lm_set_3.4_WR, trt.vs.ctrl ~isolate, ref=36)$contrasts, infer = c(T,T))
write.csv(lsm_s3.4.dun.WR, "./lsmeans_summary_tables/lsm_s3.4.dun.WR.csv")3.4.3. - CLD - (Pairwise by isolate)
CLD(lsmeans(lm_set_3.4_WR, ~isolate), alpha=0.1)'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead. isolate lsmean SE df lower.CL upper.CL .group
201070 16.4 9.69 70 -2.97 35.7 1
201007 16.8 7.91 70 1.02 32.6 1
201995 17.5 7.91 70 1.69 33.2 1
200651 17.9 7.91 70 2.12 33.7 1
200891 18.4 7.91 70 2.66 34.2 1
200308 19.9 7.91 70 4.12 35.7 1
201054 22.3 7.91 70 6.49 38.0 1
200542 22.3 7.91 70 6.52 38.1 1
200892 23.5 7.91 70 7.76 39.3 1
201620 23.7 7.91 70 7.92 39.5 1
201653 24.2 7.91 70 8.46 40.0 1
201107 24.5 7.91 70 8.69 40.2 1
201082 26.3 7.91 70 10.49 42.0 12
200790 27.0 7.91 70 11.22 42.8 12
201044 28.2 7.91 70 12.39 43.9 123
200276 29.0 7.91 70 13.22 44.8 123
200559 29.8 7.91 70 14.06 45.6 123
200993 30.2 7.91 70 14.42 46.0 123
200509 30.5 7.91 70 14.72 46.3 123
200271 31.7 7.91 70 15.92 47.5 123
201842 32.3 7.91 70 16.49 48.0 123
200437 32.3 7.91 70 16.56 48.1 123
201248 33.4 7.91 70 17.62 49.2 123
200465 33.6 7.91 70 17.82 49.4 123
201028 35.0 7.91 70 19.22 50.8 123
200646 36.1 9.69 70 16.83 55.5 1234
200278 37.5 7.91 70 21.72 53.3 1234
200785 37.9 7.91 70 22.16 53.7 1234
200317 40.8 7.91 70 25.06 56.6 1234
201461 41.2 7.91 70 25.46 57.0 1234
201293 42.8 7.91 70 26.99 58.5 1234
200944 54.6 7.91 70 38.86 70.4 1234
200464 58.7 7.91 70 42.89 74.4 1234
control 66.9 7.91 70 51.09 82.6 234
200791 69.2 7.91 70 53.39 84.9 34
200432 79.1 7.91 70 63.36 94.9 4
Confidence level used: 0.95
P value adjustment: tukey method for comparing a family of 36 estimates
significance level used: alpha = 0.1 plot(CLD(lsmeans(lm_set_3.4_WR, ~isolate), alpha=0.1), main="All pairwise comparisons for Mean Wet Root Weight", xlab = "Mean Weight (mg)", ylab = "Isolate ID")'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead.
3.4.3. - CLD - Mean Estimate Differences to Control
Compact Letter Display of estimates for Wet Root Mean weight (mg) differences between plants that were inoculated with a mucilage isolates and the non-inoculated control.
plot(CLD(lsmeans(lm_set_3.4_WR, trt.vs.ctrl~isolate,ref=36)$contrasts, alpha=0.1), main="Mean Wet Root Weight Differences of Inoculated Plants vs. Control", xlab = "Difference of Mean Estimates (mg)", ylab = "Contrasts")'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead.No information available to display confidence limits
No significant effect of inoculation treatment on mean wet root weight for batch 3.4 was observed.
3.4.4. Wet Shoot Weight
# linear model of wet shoot data
lm_set_3.4_WS <- lm(mg ~ 1 + isolate, set_3.4_WS)
op = par(mfrow=c(1,2))
plot(lm_set_3.4_WS, which = c(2,3))
par(op)
3.4.4. - Anova
# assess variance
anova(lm_set_3.4_WS)Analysis of Variance Table
Response: mg
Df Sum Sq Mean Sq F value Pr(>F)
isolate 35 942292 26923 1.9685 0.008146 **
Residuals 70 957350 13676
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 13.4.4. - LSMeans - Dunnett
lsm_s3.4.dun.WS <- summary(lsmeans(lm_set_3.4_WS, trt.vs.ctrl ~isolate, ref=36)$contrasts, infer = c(T,T))
write.csv(lsm_s3.4.dun.WS, "./lsmeans_summary_tables/lsm_s3.4.dun.WS.csv")3.4.4. - CLD - (Pairwise by isolate)
CLD(lsmeans(lm_set_3.4_WS, ~isolate), alpha=0.1)'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead. isolate lsmean SE df lower.CL upper.CL .group
201070 107 82.7 70 -57.68 272 1
200542 133 67.5 70 -1.96 267 1
201995 159 67.5 70 24.57 294 1
201007 160 67.5 70 24.87 294 1
200651 166 67.5 70 30.94 300 1
200790 168 67.5 70 33.34 303 1
200276 175 67.5 70 40.50 310 1
201107 176 67.5 70 41.54 311 1
200892 179 67.5 70 44.10 313 1
200891 198 67.5 70 62.84 332 1
201082 208 67.5 70 73.80 343 1
201653 213 67.5 70 78.34 348 1
200308 223 67.5 70 88.37 358 1
201054 244 67.5 70 109.77 379 1
200465 249 67.5 70 114.00 383 1
201028 256 67.5 70 120.87 390 1
200278 258 67.5 70 123.47 393 1
201248 259 67.5 70 123.87 393 1
200317 274 67.5 70 139.37 409 1
201044 278 67.5 70 143.64 413 1
200271 282 67.5 70 147.07 416 1
200785 284 67.5 70 149.54 419 1
201842 289 67.5 70 154.54 424 1
control 300 67.5 70 165.04 434 1
201293 307 67.5 70 172.20 442 1
200559 321 67.5 70 186.04 455 1
201461 321 67.5 70 186.30 456 1
200437 334 67.5 70 199.80 469 1
200646 376 82.7 70 211.27 541 1
201620 377 67.5 70 242.37 512 1
200993 391 67.5 70 255.97 525 1
200509 398 67.5 70 263.67 533 1
200464 430 67.5 70 295.84 565 1
200944 438 67.5 70 303.67 573 1
200432 446 67.5 70 311.67 581 1
200791 477 67.5 70 342.04 611 1
Confidence level used: 0.95
P value adjustment: tukey method for comparing a family of 36 estimates
significance level used: alpha = 0.1 plot(CLD(lsmeans(lm_set_3.4_WS, ~isolate), alpha=0.1), main="All pairwise comparisons for Mean Wet Shoot Weight", xlab = "Mean Weight (mg)", ylab = "Isolate ID")'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead.
3.4.4. - CLD - Mean Estimate Differences to Control
Compact Letter Display of estimates for Wet Shoot Mean weight (mg) differences between plants that were inoculated with a mucilage isolates and the non-inoculated control.
plot(CLD(lsmeans(lm_set_3.4_WS, trt.vs.ctrl~isolate,ref=36)$contrasts, alpha=0.1), main="Mean Wet Shoot Weight Differences of Inoculated Plants vs. Control", xlab = "Difference of Mean Estimates (mg)", ylab = "Contrasts")'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead.No information available to display confidence limits
No significant effect of inoculation treatment on mean wet shoot weight for batch 3.4 was observed. Confidence intervals for mean estimates of isolates that produced positive weight differences relative to the control are highly overlapping.
Batch 3.5
# analyze set 3.5 data
str(set_3.5)'data.frame': 456 obs. of 4 variables:
$ isolate: chr "201928" "201928" "201928" "201928" ...
$ rep : int 1 1 1 1 1 1 2 2 2 2 ...
$ sample : chr "WS" "WR" "DS" "DR" ...
$ mg : num NA NA NA NA NA NA NA NA NA NA ...#subset by sample type
set_3.5_DR <- filter(set_3.5, sample == "DR")
set_3.5_DS <- filter(set_3.5, sample == "DS")
set_3.5_WR <- filter(set_3.5, sample == "WR")
set_3.5_WS <- filter(set_3.5, sample == "WS")Experimental Design Table
| Structure | Factor | Type | # levels |
|---|---|---|---|
| Treatment | Isolate | Qualitative | 26 |
| Design | MS Media Slants | Experimental Unit | 78 |
| Response | Plant Tissue Weight | Observational Unit | 312 |
| Response | Dry Root Weight (DR) | Variable | 78 |
| Response | Dry Shoot Weight (DS) | Variable | 78 |
| Response | Wet Root Weight (WR) | Variable | 78 |
| Response | Wet Shoot Weight (WS) | Variable | 78 |
3.5.1. Dry Root Weight
# linear model of dry root data
lm_set_3.5_DR <- lm(mg ~ 1 + isolate, set_3.5_DR)
op = par(mfrow=c(1,2))
plot(lm_set_3.5_DR, which = c(2,3))not plotting observations with leverage one:
9, 58par(op)
3.5.1. - Anova
# assess variance
anova(lm_set_3.5_DR)Analysis of Variance Table
Response: mg
Df Sum Sq Mean Sq F value Pr(>F)
isolate 21 54.707 2.6051 1.593 0.1025
Residuals 39 63.778 1.6353 3.5.1. - LSMeans - Dunnett
lsm_s3.5.dun.DR <- summary(lsmeans(lm_set_3.5_DR, trt.vs.ctrl ~isolate, ref=22)$contrasts, infer = c(T,T))
write.csv(lsm_s3.5.dun.DR, "./lsmeans_summary_tables/lsm_s3.5.dun.DR.csv")3.5.1. - CLD - (Pairwise by isolate)
CLD(lsmeans(lm_set_3.5_DR, ~isolate), alpha=0.1)'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead. isolate lsmean SE df lower.CL upper.CL .group
201075 0.90 1.279 39 -1.68663 3.49 1
202001 1.13 0.738 39 -0.36006 2.63 1
201024 1.23 0.738 39 -0.26006 2.73 1
200327 1.40 0.738 39 -0.09339 2.89 1
200994 1.50 0.738 39 0.00661 2.99 1
201297 1.60 1.279 39 -0.98663 4.19 1
200593 1.67 0.738 39 0.17328 3.16 1
201997 1.70 0.738 39 0.20661 3.19 1
200508 1.70 0.738 39 0.20661 3.19 1
200440 1.73 0.738 39 0.23994 3.23 1
201258 1.77 0.738 39 0.27328 3.26 1
200267 1.80 0.738 39 0.30661 3.29 1
201947 2.10 0.738 39 0.60661 3.59 1
Control 2.63 0.738 39 1.13994 4.13 1
200315 2.73 0.738 39 1.23994 4.23 1
200302 2.80 0.738 39 1.30661 4.29 1
201058 3.43 0.738 39 1.93994 4.93 1
201443 3.47 0.738 39 1.97328 4.96 1
200735 3.63 0.738 39 2.13994 5.13 1
200514 3.83 0.738 39 2.33994 5.33 1
200624 3.85 0.904 39 2.02098 5.68 1
200931 3.90 0.738 39 2.40661 5.39 1
Confidence level used: 0.95
P value adjustment: tukey method for comparing a family of 22 estimates
significance level used: alpha = 0.1 plot(CLD(lsmeans(lm_set_3.5_DR, ~isolate), alpha=0.1), main="All pairwise comparisons for Mean Dry Root Weight", xlab = "Mean Weight (mg)", ylab = "Isolate ID")'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead.
3.5.1. - CLD - Mean Estimate Differences to Control
Compact Letter Display of estimates for Dry Root Mean weight (mg) differences between plants that were inoculated with a mucilage isolates and the non-inoculated control.
plot(CLD(lsmeans(lm_set_3.5_DR, trt.vs.ctrl~isolate,ref=22)$contrasts, alpha=0.1), main="Mean Dry Root Weight Differences of Inoculated Plants vs. Control", xlab = "Difference of Mean Estimates (mg)", ylab = "Contrasts")'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead.No information available to display confidence limits
No significant effect.
3.5.2. Dry Shoot Weight
# linear model of dry shoot data
lm_set_3.5_DS <- lm(mg ~ 1 + isolate, set_3.5_DS)
op = par(mfrow=c(1,2))
plot(lm_set_3.5_DS, which = c(2,3))not plotting observations with leverage one:
9, 58par(op)
3.5.2. - Anova
# assess variance
anova(lm_set_3.5_DS)Analysis of Variance Table
Response: mg
Df Sum Sq Mean Sq F value Pr(>F)
isolate 21 765.1 36.434 0.8293 0.6705
Residuals 39 1713.3 43.931 3.5.2. - LSMeans - Dunnett
lsm_s3.5.dun.DS <- summary(lsmeans(lm_set_3.5_DS, trt.vs.ctrl ~isolate, ref=22)$contrasts, infer = c(T,T))
write.csv(lsm_s3.5.dun.DS, "./lsmeans_summary_tables/lsm_s3.5.dun.DS.csv")3.5.2. - CLD - (Pairwise by isolate)
CLD(lsmeans(lm_set_3.5_DS, ~isolate), alpha=0.1)'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead. isolate lsmean SE df lower.CL upper.CL .group
201024 8.97 3.83 39 1.23 16.7 1
200327 9.60 3.83 39 1.86 17.3 1
201075 11.20 6.63 39 -2.21 24.6 1
200994 12.93 3.83 39 5.19 20.7 1
201443 12.97 3.83 39 5.23 20.7 1
200593 13.27 3.83 39 5.53 21.0 1
200302 15.13 3.83 39 7.39 22.9 1
201058 15.13 3.83 39 7.39 22.9 1
202001 15.23 3.83 39 7.49 23.0 1
Control 15.80 3.83 39 8.06 23.5 1
200440 16.30 3.83 39 8.56 24.0 1
201947 16.60 3.83 39 8.86 24.3 1
201997 16.87 3.83 39 9.13 24.6 1
201258 17.07 3.83 39 9.33 24.8 1
200267 17.47 3.83 39 9.73 25.2 1
200514 18.03 3.83 39 10.29 25.8 1
200508 18.63 3.83 39 10.89 26.4 1
200315 19.57 3.83 39 11.83 27.3 1
200735 19.80 3.83 39 12.06 27.5 1
201297 21.00 6.63 39 7.59 34.4 1
200624 21.30 4.69 39 11.82 30.8 1
200931 23.70 3.83 39 15.96 31.4 1
Confidence level used: 0.95
P value adjustment: tukey method for comparing a family of 22 estimates
significance level used: alpha = 0.1 plot(CLD(lsmeans(lm_set_3.5_DS, ~isolate), alpha=0.1), main="All pairwise comparisons for Mean Dry Shoot Weight", xlab = "Mean Weight (mg)", ylab = "Isolate ID")'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead.
3.5.2. - CLD - Mean Estimate Differences to Control
Compact Letter Display of estimates for Dry Shoot Mean weight (mg) differences between plants that were inoculated with a mucilage isolates and the non-inoculated control.
plot(CLD(lsmeans(lm_set_3.5_DS, trt.vs.ctrl~isolate,ref=22)$contrasts, alpha=0.1), main="Mean Dry Shoot Weight Differences of Inoculated Plants vs. Control", xlab = "Difference of Mean Estimates (mg)", ylab = "Contrasts")'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead.No information available to display confidence limits
No significant positive effects were observed for mean dry shoot weight in batch 3.5.
3.5.3. Wet Root Weight
# linear model of wet root data
lm_set_3.5_WR <- lm(mg ~ 1 + isolate, set_3.5_WR)
op = par(mfrow=c(1,2))
plot(lm_set_3.5_WR, which = c(2,3))not plotting observations with leverage one:
9, 58par(op)
3.5.3. - Anova
# assess variance
anova(lm_set_3.5_WR)Analysis of Variance Table
Response: mg
Df Sum Sq Mean Sq F value Pr(>F)
isolate 21 12426 591.72 1.4796 0.1423
Residuals 39 15596 399.91 3.5.3. - LSMeans - Dunnett
lsm_s3.5.dun.WR <- summary(lsmeans(lm_set_3.5_WR, trt.vs.ctrl ~isolate, ref=22)$contrasts, infer = c(T,T))
write.csv(lsm_s3.5.dun.WR, "./lsmeans_summary_tables/lsm_s3.5.dun.WR.csv")3.5.3. - CLD - (Pairwise by isolate)
CLD(lsmeans(lm_set_3.5_WR, ~isolate), alpha=0.1)'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead. isolate lsmean SE df lower.CL upper.CL .group
202001 16.5 11.5 39 -6.887 39.8 1
201024 17.0 11.5 39 -6.320 40.4 1
201075 17.6 20.0 39 -22.849 58.0 1
200327 19.2 11.5 39 -4.153 42.6 1
201997 22.5 11.5 39 -0.887 45.8 1
201258 24.7 11.5 39 1.313 48.0 1
200994 25.9 11.5 39 2.580 49.3 1
200440 27.7 11.5 39 4.313 51.0 1
200508 27.9 11.5 39 4.547 51.3 1
200267 29.1 11.5 39 5.713 52.4 1
200593 31.1 11.5 39 7.713 54.4 1
201297 32.3 20.0 39 -8.149 72.7 1
201947 32.4 11.5 39 9.080 55.8 1
200302 36.2 11.5 39 12.880 59.6 1
200315 39.3 11.5 39 15.980 62.7 1
201058 42.2 11.5 39 18.847 65.6 1
200735 47.5 11.5 39 24.113 70.8 1
201443 51.7 11.5 39 28.313 75.0 1
200624 56.0 14.1 39 27.348 84.6 1
200514 58.5 11.5 39 35.147 81.9 1
200931 59.8 11.5 39 36.447 83.2 1
Control 60.6 11.5 39 37.280 84.0 1
Confidence level used: 0.95
P value adjustment: tukey method for comparing a family of 22 estimates
significance level used: alpha = 0.1 plot(CLD(lsmeans(lm_set_3.5_WR, ~isolate), alpha=0.1), main="All pairwise comparisons for Mean Wet Root Weight", xlab = "Mean Weight (mg)", ylab = "Isolate ID")'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead.
3.5.3. - CLD - Mean Estimate Differences to Control
Compact Letter Display of estimates for Wet Root Mean weight (mg) differences between plants that were inoculated with a mucilage isolates and the non-inoculated control.
plot(CLD(lsmeans(lm_set_3.5_WR, trt.vs.ctrl~isolate,ref=22)$contrasts, alpha=0.1), main="Mean Wet Root Weight Differences of Inoculated Plants vs. Control", xlab = "Difference of Mean Estimates (mg)", ylab = "Contrasts")'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead.No information available to display confidence limits
No significant positive effects observed for wet root weight of batch 3.5.
3.5.4. Wet Shoot Weight
# linear model of wet shoot data
lm_set_3.5_WS <- lm(mg ~ 1 + isolate, set_3.5_WS)
op = par(mfrow=c(1,2))
plot(lm_set_3.5_WS, which = c(2,3))not plotting observations with leverage one:
9, 58par(op)
3.5.4. - Anova
# assess variance
anova(lm_set_3.5_WS)Analysis of Variance Table
Response: mg
Df Sum Sq Mean Sq F value Pr(>F)
isolate 21 271402 12924 0.8343 0.6651
Residuals 39 604167 15492 3.5.4. - LSMeans - Dunnett
lsm_s3.5.dun.WS <- summary(lsmeans(lm_set_3.5_WS, trt.vs.ctrl ~isolate, ref=22)$contrasts, infer = c(T,T))
write.csv(lsm_s3.5.dun.WS, "./lsmeans_summary_tables/lsm_s3.5.dun.WS.csv")3.5.4. - CLD - (Pairwise by isolate)
CLD(lsmeans(lm_set_3.5_WS, ~isolate), alpha=0.1)'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead. isolate lsmean SE df lower.CL upper.CL .group
201024 139 71.9 39 -6.65 284 1
200327 140 71.9 39 -4.95 286 1
201075 160 124.5 39 -91.55 412 1
201443 193 71.9 39 47.98 339 1
200994 217 71.9 39 71.88 363 1
200302 246 71.9 39 101.02 392 1
201258 252 71.9 39 106.18 397 1
200593 255 71.9 39 109.78 400 1
201058 264 71.9 39 118.78 409 1
200440 269 71.9 39 123.85 415 1
202001 272 71.9 39 126.25 417 1
201997 279 71.9 39 134.05 425 1
201947 289 71.9 39 143.85 435 1
200514 293 71.9 39 148.08 439 1
201297 304 124.5 39 52.25 556 1
200267 308 71.9 39 162.25 453 1
200315 321 71.9 39 175.18 466 1
200508 326 71.9 39 181.15 472 1
200735 330 71.9 39 184.65 475 1
Control 347 71.9 39 201.75 492 1
200624 365 88.0 39 186.78 543 1
200931 407 71.9 39 261.25 552 1
Confidence level used: 0.95
P value adjustment: tukey method for comparing a family of 22 estimates
significance level used: alpha = 0.1 plot(CLD(lsmeans(lm_set_3.5_WS, ~isolate), alpha=0.1), main="All pairwise comparisons for Mean Wet Shoot Weight", xlab = "Mean Weight (mg)", ylab = "Isolate ID")'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead.
3.5.4. - CLD - Mean Estimate Differences to Control
Compact Letter Display of estimates for Wet Shoot Mean weight (mg) differences between plants that were inoculated with a mucilage isolates and the non-inoculated control.
plot(CLD(lsmeans(lm_set_3.5_WS, trt.vs.ctrl~isolate,ref=22)$contrasts, alpha=0.1), main="Mean Wet Shoot Weight Differences of Inoculated Plants vs. Control", xlab = "Difference of Mean Estimates (mg)", ylab = "Contrasts")'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead.No information available to display confidence limits
No significant positive effect was observed for Wet Shoot weight in batch 3.5 inoculations.
Batch 4.1
# analyze set 4.1 data
str(set_4.1)'data.frame': 690 obs. of 4 variables:
$ isolate: chr "200660" "200660" "200660" "200660" ...
$ rep : int 1 1 1 1 1 1 2 2 2 2 ...
$ sample : chr "WS" "WR" "DS" "DR" ...
$ mg : num 193.2 8.9 15.8 1.1 58557.8 ...#subset by sample type
set_4.1_DR <- filter(set_4.1, sample == "DR")
set_4.1_DS <- filter(set_4.1, sample == "DS")
set_4.1_WR <- filter(set_4.1, sample == "WR")
set_4.1_WS <- filter(set_4.1, sample == "WS")Experimental Design Table
| Structure | Factor | Type | # levels |
|---|---|---|---|
| Treatment | Isolate | Qualitative | 39 |
| Design | MS Media Slants | Experimental Unit | 117 |
| Response | Plant Tissue Weight | Observational Unit | 468 |
| Response | Dry Root Weight (DR) | Variable | 117 |
| Response | Dry Shoot Weight (DS) | Variable | 117 |
| Response | Wet Root Weight (WR) | Variable | 117 |
| Response | Wet Shoot Weight (WS) | Variable | 117 |
4.1.1. Dry Root Weight
# linear model of dry root data
lm_set_4.1_DR <- lm(mg ~ 1 + isolate, set_4.1_DR)
op = par(mfrow=c(1,2))
plot(lm_set_4.1_DR, which = c(2,3))
par(op)
4.1.1. - Anova
# assess variance
anova(lm_set_4.1_DR)Analysis of Variance Table
Response: mg
Df Sum Sq Mean Sq F value Pr(>F)
isolate 36 70.319 1.9533 1.2786 0.1874
Residuals 71 108.463 1.5276 4.1.1. - LSMeans - Dunnett
lsm_s4.1.dun.DR <- summary(lsmeans(lm_set_4.1_DR, trt.vs.ctrl ~isolate, ref=37)$contrasts, infer = c(T,T))
write.csv(lsm_s4.1.dun.DR, "./lsmeans_summary_tables/lsm_s4.1.dun.DR.csv")4.1.1. - CLD - (Pairwise by isolate)
CLD(lsmeans(lm_set_4.1_DR, ~isolate), alpha=0.1)'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead. isolate lsmean SE df lower.CL upper.CL .group
200663 0.767 0.714 71 -0.6562 2.19 1
200915 0.800 0.714 71 -0.6229 2.22 1
201155 1.000 0.874 71 -0.7426 2.74 1
200775 1.100 0.714 71 -0.3229 2.52 1
201890 1.233 0.714 71 -0.1895 2.66 1
200951 1.267 0.714 71 -0.1562 2.69 1
200577 1.300 0.714 71 -0.1229 2.72 1
200660 1.367 0.714 71 -0.0562 2.79 1
200973 1.400 0.714 71 -0.0229 2.82 1
200800 1.433 0.714 71 0.0105 2.86 1
201291 1.450 0.874 71 -0.2926 3.19 1
201186 1.467 0.714 71 0.0438 2.89 1
201292 1.500 0.714 71 0.0771 2.92 1
201945 1.500 0.714 71 0.0771 2.92 1
200527 1.700 0.714 71 0.2771 3.12 1
200319 1.767 0.714 71 0.3438 3.19 1
200553 1.800 0.714 71 0.3771 3.22 1
201662 1.800 0.714 71 0.3771 3.22 1
200587 1.833 0.714 71 0.4105 3.26 1
200589 1.900 0.714 71 0.4771 3.32 1
200458 1.967 0.714 71 0.5438 3.39 1
200294 1.967 0.714 71 0.5438 3.39 1
200874 1.967 0.714 71 0.5438 3.39 1
200268 2.000 0.714 71 0.5771 3.42 1
200545 2.000 0.714 71 0.5771 3.42 1
201238 2.133 0.714 71 0.7105 3.56 1
202006 2.200 0.714 71 0.7771 3.62 1
200814 2.567 0.714 71 1.1438 3.99 1
200738 2.667 0.714 71 1.2438 4.09 1
200984 2.933 0.714 71 1.5105 4.36 1
201103 2.933 0.714 71 1.5105 4.36 1
200561 2.967 0.714 71 1.5438 4.39 1
200910 3.250 0.874 71 1.5074 4.99 1
201654 3.367 0.714 71 1.9438 4.79 1
200311 3.533 0.714 71 2.1105 4.96 1
control 3.600 0.714 71 2.1771 5.02 1
200471 3.933 0.714 71 2.5105 5.36 1
Confidence level used: 0.95
P value adjustment: tukey method for comparing a family of 37 estimates
significance level used: alpha = 0.1 plot(CLD(lsmeans(lm_set_4.1_DR, ~isolate), alpha=0.1), main="All pairwise comparisons for Mean Dry Root Weight", xlab = "Mean Weight (mg)", ylab = "Isolate ID")'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead.
4.1.1. - CLD - Mean Estimate Differences to Control
Compact Letter Display of estimates for Dry Root Mean weight (mg) differences between plants that were inoculated with a mucilage isolates and the non-inoculated control.
plot(CLD(lsmeans(lm_set_4.1_DR, trt.vs.ctrl~isolate,ref=37)$contrasts, alpha=0.1), main="Mean Dry Root Weight Differences of Inoculated Plants vs. Control", xlab = "Difference of Mean Estimates (mg)", ylab = "Contrasts")'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead.No information available to display confidence limits
No significant positive effect observed for inoculum treatment based on dry root measurements of batch 4.1.
4.1.2. Dry Shoot Weight
# linear model of dry shoot data
lm_set_4.1_DS <- lm(mg ~ 1 + isolate, set_4.1_DS)
op = par(mfrow=c(1,2))
plot(lm_set_4.1_DS, which = c(2,3))
par(op)
4.1.2. - Anova
# assess variance
anova(lm_set_4.1_DS)Analysis of Variance Table
Response: mg
Df Sum Sq Mean Sq F value Pr(>F)
isolate 36 3372.5 93.680 1.7043 0.02811 *
Residuals 71 3902.7 54.968
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 14.1.2. - LSMeans - Dunnett
lsm_s4.1.dun.DS <- summary(lsmeans(lm_set_4.1_DS, trt.vs.ctrl ~isolate, ref=37)$contrasts, infer = c(T,T))
write.csv(lsm_s4.1.dun.DS, "./lsmeans_summary_tables/lsm_s4.1.dun.DS.csv")4.1.2. - CLD - (Pairwise by isolate)
CLD(lsmeans(lm_set_4.1_DS, ~isolate), alpha=0.1)'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead. isolate lsmean SE df lower.CL upper.CL .group
200915 8.4 4.28 71 -0.135 16.9 1
200775 10.4 4.28 71 1.898 19.0 12
200294 10.6 4.28 71 2.065 19.1 12
201890 11.7 4.28 71 3.165 20.2 12
202006 12.3 4.28 71 3.798 20.9 12
201292 12.7 4.28 71 4.198 21.3 12
200458 13.2 4.28 71 4.632 21.7 12
200527 13.2 4.28 71 4.632 21.7 12
200589 13.8 4.28 71 5.265 22.3 12
201103 14.8 4.28 71 6.265 23.3 12
200311 14.9 4.28 71 6.332 23.4 12
200951 15.0 4.28 71 6.498 23.6 12
200319 15.8 4.28 71 7.232 24.3 12
200660 15.9 4.28 71 7.398 24.5 12
201291 15.9 5.24 71 5.497 26.4 12
200663 16.6 4.28 71 8.032 25.1 12
200973 17.6 4.28 71 9.032 26.1 12
control 17.7 4.28 71 9.132 26.2 12
200577 17.7 4.28 71 9.132 26.2 12
200553 19.5 4.28 71 10.965 28.0 12
200910 20.4 5.24 71 9.897 30.8 12
200874 20.4 4.28 71 11.865 28.9 12
201662 20.6 4.28 71 12.065 29.1 12
201945 20.8 4.28 71 12.298 29.4 12
201155 21.1 5.24 71 10.647 31.6 12
200268 21.5 4.28 71 12.998 30.1 12
200545 22.2 4.28 71 13.665 30.7 12
200471 22.5 4.28 71 13.998 31.1 12
201186 22.7 4.28 71 14.198 31.3 12
200800 23.1 4.28 71 14.598 31.7 12
201238 23.3 4.28 71 14.732 31.8 12
200587 24.3 4.28 71 15.732 32.8 12
200561 25.2 4.28 71 16.665 33.7 12
201654 26.3 4.28 71 17.732 34.8 12
200814 27.3 4.28 71 18.765 35.8 12
200738 29.3 4.28 71 20.732 37.8 12
200984 32.0 4.28 71 23.432 40.5 2
Confidence level used: 0.95
P value adjustment: tukey method for comparing a family of 37 estimates
significance level used: alpha = 0.1 plot(CLD(lsmeans(lm_set_4.1_DS, ~isolate), alpha=0.1), main="All pairwise comparisons for Mean Dry Shoot Weight", xlab = "Mean Weight (mg)", ylab = "Isolate ID")'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead.
4.1.2. - CLD - Mean Estimate Differences to Control
Compact Letter Display of estimates for Dry Shoot Mean weight (mg) differences between plants that were inoculated with a mucilage isolates and the non-inoculated control.
plot(CLD(lsmeans(lm_set_4.1_DS, trt.vs.ctrl~isolate,ref=37)$contrasts, alpha=0.1), main="Mean Dry Shoot Weight Differences of Inoculated Plants vs. Control", xlab = "Difference of Mean Estimates (mg)", ylab = "Contrasts")'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead.No information available to display confidence limits
There are no significant positive effects observed based on measurements of dry shoot weight for plants inoculated with isolates in batch 4.1.
4.1.3. Wet Root Weight
# linear model of wet root data
lm_set_4.1_WR <- lm(mg ~ 1 + isolate, set_4.1_WR)
op = par(mfrow=c(1,2))
plot(lm_set_4.1_WR, which = c(2,3))
par(op)
4.1.3. - Anova
# assess variance
anova(lm_set_4.1_WR)Analysis of Variance Table
Response: mg
Df Sum Sq Mean Sq F value Pr(>F)
isolate 36 9240.3 256.68 1.6359 0.03899 *
Residuals 71 11140.1 156.90
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 14.1.3. - LSMeans - Dunnett
lsm_s4.1.dun.WR <- summary(lsmeans(lm_set_4.1_WR, trt.vs.ctrl ~isolate, ref=37)$contrasts, infer = c(T,T))
write.csv(lsm_s4.1.dun.WR, "./lsmeans_summary_tables/lsm_s4.1.dun.WR.csv")4.1.3. - CLD - (Pairwise by isolate)
CLD(lsmeans(lm_set_4.1_WR, ~isolate), alpha=0.1)'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead. isolate lsmean SE df lower.CL upper.CL .group
200663 9.13 7.23 71 -5.287 23.6 1
201291 11.55 8.86 71 -6.111 29.2 1
200577 12.33 7.23 71 -2.087 26.8 1
200660 13.30 7.23 71 -1.120 27.7 1
200915 13.80 7.23 71 -0.620 28.2 1
201155 13.90 8.86 71 -3.761 31.6 1
201186 14.57 7.23 71 0.147 29.0 1
201292 15.40 7.23 71 0.980 29.8 1
200800 15.40 7.23 71 0.980 29.8 1
201890 15.80 7.23 71 1.380 30.2 1
200951 16.10 7.23 71 1.680 30.5 1
200973 16.33 7.23 71 1.913 30.8 1
200775 16.87 7.23 71 2.447 31.3 1
200319 19.80 7.23 71 5.380 34.2 1
200294 20.17 7.23 71 5.747 34.6 1
201945 20.37 7.23 71 5.947 34.8 1
200587 20.97 7.23 71 6.547 35.4 1
201662 21.00 7.23 71 6.580 35.4 1
200553 21.90 7.23 71 7.480 36.3 1
200527 22.00 7.23 71 7.580 36.4 1
200268 22.07 7.23 71 7.647 36.5 1
200874 23.83 7.23 71 9.413 38.3 1
201238 24.23 7.23 71 9.813 38.7 1
202006 24.30 7.23 71 9.880 38.7 1
200545 25.43 7.23 71 11.013 39.9 1
200589 26.33 7.23 71 11.913 40.8 1
201103 26.33 7.23 71 11.913 40.8 1
200458 27.67 7.23 71 13.247 42.1 1
200814 29.63 7.23 71 15.213 44.1 1
200561 30.60 7.23 71 16.180 45.0 1
200738 33.73 7.23 71 19.313 48.2 1
201654 37.23 7.23 71 22.813 51.7 1
200910 37.30 8.86 71 19.639 55.0 1
200984 39.70 7.23 71 25.280 54.1 1
200471 41.20 7.23 71 26.780 55.6 1
200311 42.03 7.23 71 27.613 56.5 1
control 44.80 7.23 71 30.380 59.2 1
Confidence level used: 0.95
P value adjustment: tukey method for comparing a family of 37 estimates
significance level used: alpha = 0.1 plot(CLD(lsmeans(lm_set_4.1_WR, ~isolate), alpha=0.1), main="All pairwise comparisons for Mean Wet Root Weight", xlab = "Mean Weight (mg)", ylab = "Isolate ID")'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead.
4.1.3. - CLD - Mean Estimate Differences to Control
Compact Letter Display of estimates for Wet Root Mean weight (mg) differences between plants that were inoculated with a mucilage isolates and the non-inoculated control.
plot(CLD(lsmeans(lm_set_4.1_WR, trt.vs.ctrl~isolate,ref=37)$contrasts, alpha=0.1), main="Mean Wet Root Weight Differences of Inoculated Plants vs. Control", xlab = "Difference of Mean Estimates (mg)", ylab = "Contrasts")'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead.No information available to display confidence limits
Control is the highest mean estimate. All significant effects are negative on mean wet root weight.
4.1.4. Wet Shoot Weight
# linear model of wet shoot data
lm_set_4.1_WS <- lm(mg ~ 1 + isolate, set_4.1_WS)
op = par(mfrow=c(1,2))
plot(lm_set_4.1_WS, which = c(2,3))
par(op)
4.1.4. - Anova
# assess variance
anova(lm_set_4.1_WS)Analysis of Variance Table
Response: mg
Df Sum Sq Mean Sq F value Pr(>F)
isolate 36 825779 22938 1.6896 0.03018 *
Residuals 71 963933 13576
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 14.1.4. - LSMeans - Dunnett
lsm_s4.1.dun.WS <- summary(lsmeans(lm_set_4.1_WS, trt.vs.ctrl ~isolate, ref=37)$contrasts, infer = c(T,T))
write.csv(lsm_s4.1.dun.WS, "./lsmeans_summary_tables/lsm_s4.1.dun.WS.csv")4.1.4. - CLD - (Pairwise by isolate)
CLD(lsmeans(lm_set_4.1_WS, ~isolate), alpha=0.1)'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead. isolate lsmean SE df lower.CL upper.CL .group
200294 132 67.3 71 -2.34 266 1
200775 142 67.3 71 7.56 276 1
200915 152 67.3 71 18.03 286 1
201890 156 67.3 71 22.36 291 1
202006 157 67.3 71 22.40 291 1
200589 171 67.3 71 37.33 306 12
200527 174 67.3 71 40.33 309 12
201292 181 67.3 71 46.43 315 12
200951 200 67.3 71 66.10 334 12
200458 201 67.3 71 67.23 336 12
200319 203 67.3 71 68.73 337 12
201291 206 82.4 71 42.02 371 12
200660 212 67.3 71 77.53 346 12
201103 216 67.3 71 82.20 350 12
200311 225 67.3 71 91.16 359 12
200663 230 67.3 71 96.20 364 12
200577 232 67.3 71 98.13 366 12
201155 258 82.4 71 94.02 423 12
200973 266 67.3 71 132.16 400 12
200874 268 67.3 71 133.66 402 12
201662 268 67.3 71 134.30 403 12
200910 276 82.4 71 111.92 440 12
control 280 67.3 71 146.23 415 12
201186 283 67.3 71 148.70 417 12
200553 300 67.3 71 165.83 434 12
200268 305 67.3 71 170.86 439 12
201945 313 67.3 71 178.83 447 12
201238 317 67.3 71 182.53 451 12
200545 318 67.3 71 183.73 452 12
200587 326 67.3 71 192.13 460 12
200471 331 67.3 71 196.90 465 12
200800 357 67.3 71 222.66 491 12
201654 357 67.3 71 222.73 491 12
200561 358 67.3 71 223.60 492 12
200814 381 67.3 71 246.83 515 12
200738 440 67.3 71 305.40 574 12
200984 529 67.3 71 395.16 663 2
Confidence level used: 0.95
P value adjustment: tukey method for comparing a family of 37 estimates
significance level used: alpha = 0.1 plot(CLD(lsmeans(lm_set_4.1_WS, ~isolate), alpha=0.1), main="All pairwise comparisons for Mean Wet Shoot Weight", xlab = "Mean Weight (mg)", ylab = "Isolate ID")'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead.
4.1.4. - CLD - Mean Estimate Differences to Control
Compact Letter Display of estimates for Wet Root Mean weight (mg) differences between plants that were inoculated with a mucilage isolates and the non-inoculated control.
plot(CLD(lsmeans(lm_set_4.1_WS, trt.vs.ctrl~isolate,ref=37)$contrasts, alpha=0.1), main="Mean Wet Root Weight Differences of Inoculated Plants vs. Control", xlab = "Difference of Mean Estimates (mg)", ylab = "Contrasts")'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead.No information available to display confidence limits
Highest mean producing isolate (BCW200984) has lower Confidence limit that overlaps with upper of control.
Batch 4.2
# analyze set 4.2 data
str(set_4.2)'data.frame': 600 obs. of 4 variables:
$ isolate: chr "201813" "201813" "201813" "201813" ...
$ rep : int 1 1 1 1 1 1 2 2 2 2 ...
$ sample : chr "WS" "WR" "DS" "DR" ...
$ mg : num NA NA NA NA 58453 ...#subset by sample type
set_4.2_DR <- filter(set_4.2, sample == "DR")
set_4.2_DS <- filter(set_4.2, sample == "DS")
set_4.2_WR <- filter(set_4.2, sample == "WR")
set_4.2_WS <- filter(set_4.2, sample == "WS")Experimental Design Table
| Structure | Factor | Type | # levels |
|---|---|---|---|
| Treatment | Isolate | Qualitative | 33 |
| Design | MS Media Slants | Experimental Unit | 99 |
| Response | Plant Tissue Weight | Observational Unit | 396 |
| Response | Dry Root Weight (DR) | Variable | 99 |
| Response | Dry Shoot Weight (DS) | Variable | 99 |
| Response | Wet Root Weight (WR) | Variable | 99 |
| Response | Wet Shoot Weight (WS) | Variable | 99 |
4.2.1. Dry Root Weight
# linear model of dry root data
lm_set_4.2_DR <- lm(mg ~ 1 + isolate, set_4.2_DR)
op = par(mfrow=c(1,2))
plot(lm_set_4.2_DR, which = c(2,3))not plotting observations with leverage one:
19par(op)
4.2.1. - Anova
# assess variance
anova(lm_set_4.2_DR)Analysis of Variance Table
Response: mg
Df Sum Sq Mean Sq F value Pr(>F)
isolate 23 29.289 1.27345 1.7851 0.04461 *
Residuals 49 34.955 0.71337
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 14.2.1. - LSMeans - Dunnett
lsm_s4.2.dun.DR <- summary(lsmeans(lm_set_4.2_DR, trt.vs.ctrl ~isolate, ref=24)$contrasts, infer = c(T,T))
write.csv(lsm_s4.2.dun.DR, "./lsmeans_summary_tables/lsm_s4.2.dun.DR.csv")4.2.1. - CLD - (Pairwise by isolate)
CLD(lsmeans(lm_set_4.2_DR, ~isolate), alpha=0.1)'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead. isolate lsmean SE df lower.CL upper.CL .group
control 0.833 0.488 49 -0.147 1.81 1
201917 1.100 0.488 49 0.120 2.08 12
201990 1.100 0.488 49 0.120 2.08 12
200556 1.200 0.488 49 0.220 2.18 12
200715 1.200 0.488 49 0.220 2.18 12
201933 1.300 0.488 49 0.320 2.28 12
201888 1.433 0.488 49 0.453 2.41 12
201870 1.500 0.845 49 -0.197 3.20 12
201887 1.567 0.488 49 0.587 2.55 12
200808 1.633 0.488 49 0.653 2.61 12
201818 1.633 0.488 49 0.653 2.61 12
201880 1.683 0.345 49 0.990 2.38 12
201910 1.767 0.488 49 0.787 2.75 12
201895 1.833 0.488 49 0.853 2.81 12
201868 1.867 0.488 49 0.887 2.85 12
200533 1.900 0.488 49 0.920 2.88 12
201874 1.933 0.488 49 0.953 2.91 12
200444 2.200 0.488 49 1.220 3.18 12
201909 2.200 0.488 49 1.220 3.18 12
201899 2.367 0.488 49 1.387 3.35 12
201823 2.467 0.488 49 1.487 3.45 12
201085 2.467 0.488 49 1.487 3.45 12
201084 3.267 0.488 49 2.287 4.25 12
201815 3.500 0.488 49 2.520 4.48 2
Confidence level used: 0.95
P value adjustment: tukey method for comparing a family of 24 estimates
significance level used: alpha = 0.1 plot(CLD(lsmeans(lm_set_4.2_DR, ~isolate), alpha=0.1), main="All pairwise comparisons for Mean Dry Root Weight", xlab = "Mean Weight (mg)", ylab = "Isolate ID")'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead.
4.2.1. - CLD - Mean Estimate Differences to Control
Compact Letter Display of estimates for Dry Root Mean weight (mg) differences between plants that were inoculated with a mucilage isolates and the non-inoculated control.
plot(CLD(lsmeans(lm_set_4.2_DR, trt.vs.ctrl~isolate,ref=24)$contrasts, alpha=0.1), main="Mean Dry Root Weight Differences of Inoculated Plants vs. Control", xlab = "Difference of Mean Estimates (mg)", ylab = "Contrasts")'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead.No information available to display confidence limits
BCW201815 and BCW201084 inoculated plants had higher mean dry root weights than the control, and the confidence intervals for these estimates do not overlap with those of the control mean.
4.2.2. Dry Shoot Weight
# linear model of dry shoot data
lm_set_4.2_DS <- lm(mg ~ 1 + isolate, set_4.2_DS)
op = par(mfrow=c(1,2))
plot(lm_set_4.2_DS, which = c(2,3))not plotting observations with leverage one:
19par(op)
4.2.2. - Anova
# assess variance
anova(lm_set_4.2_DS)Analysis of Variance Table
Response: mg
Df Sum Sq Mean Sq F value Pr(>F)
isolate 23 1664.5 72.369 0.8848 0.6157
Residuals 49 4007.9 81.794 4.2.2. - LSMeans - Dunnett
lsm_s4.2.dun.DS <- summary(lsmeans(lm_set_4.2_DS, trt.vs.ctrl ~isolate, ref=24)$contrasts, infer = c(T,T))
write.csv(lsm_s4.2.dun.DS, "./lsmeans_summary_tables/lsm_s4.2.dun.DS.csv")4.2.2. - CLD - (Pairwise by isolate)
CLD(lsmeans(lm_set_4.2_DS, ~isolate), alpha=0.1)'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead. isolate lsmean SE df lower.CL upper.CL .group
control 14.9 5.22 49 4.37 25.4 1
200556 16.2 5.22 49 5.71 26.7 1
201874 18.0 5.22 49 7.47 28.5 1
201818 18.4 5.22 49 7.87 28.9 1
200533 19.5 5.22 49 9.04 30.0 1
201888 19.8 5.22 49 9.27 30.3 1
201910 19.8 5.22 49 9.31 30.3 1
201895 20.8 5.22 49 10.31 31.3 1
201870 21.0 9.04 49 2.83 39.2 1
200715 21.0 5.22 49 10.54 31.5 1
201917 22.0 5.22 49 11.51 32.5 1
201887 22.8 5.22 49 12.27 33.3 1
201880 24.0 3.69 49 16.55 31.4 1
201933 24.4 5.22 49 13.94 34.9 1
201909 24.6 5.22 49 14.14 35.1 1
201868 24.8 5.22 49 14.27 35.3 1
201990 25.7 5.22 49 15.24 36.2 1
200444 26.9 5.22 49 16.37 37.4 1
200808 26.9 5.22 49 16.44 37.4 1
201084 27.4 5.22 49 16.94 37.9 1
201823 28.7 5.22 49 18.17 39.2 1
201085 29.3 5.22 49 18.77 39.8 1
201899 30.2 5.22 49 19.74 40.7 1
201815 36.0 5.22 49 25.47 46.5 1
Confidence level used: 0.95
P value adjustment: tukey method for comparing a family of 24 estimates
significance level used: alpha = 0.1 plot(CLD(lsmeans(lm_set_4.2_DS, ~isolate), alpha=0.1), main="All pairwise comparisons for Mean Dry Shoot Weight", xlab = "Mean Weight (mg)", ylab = "Isolate ID")'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead.
4.2.2. - CLD - Mean Estimate Differences to Control
Compact Letter Display of estimates for Dry Shoot Mean weight (mg) differences between plants that were inoculated with a mucilage isolates and the non-inoculated control.
plot(CLD(lsmeans(lm_set_4.2_DS, trt.vs.ctrl~isolate,ref=24)$contrasts, alpha=0.1), main="Mean Dry Shoot Weight Differences of Inoculated Plants vs. Control", xlab = "Difference of Mean Estimates (mg)", ylab = "Contrasts")'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead.No information available to display confidence limits
No significant effect observed for dry shoot weight of batch 4.2 at an alpha level of 0.1, but
BCW201815is very close to having non-overlapping CI for the mean estimate.
4.2.3. Wet Root Weight
# linear model of wet root data
lm_set_4.2_WR <- lm(mg ~ 1 + isolate, set_4.2_WR)
op = par(mfrow=c(1,2))
plot(lm_set_4.2_WR, which = c(2,3))not plotting observations with leverage one:
19par(op)
4.2.3. - Anova
# assess variance
anova(lm_set_4.2_WR)Analysis of Variance Table
Response: mg
Df Sum Sq Mean Sq F value Pr(>F)
isolate 23 3760.6 163.51 1.4824 0.1231
Residuals 49 5404.5 110.30 4.2.3. - LSMeans - Dunnett
lsm_s4.2.dun.WR <- summary(lsmeans(lm_set_4.2_WR, trt.vs.ctrl ~isolate, ref=24)$contrasts, infer = c(T,T))
write.csv(lsm_s4.2.dun.WR, "./lsmeans_summary_tables/lsm_s4.2.dun.WR.csv")4.2.3. - CLD - (Pairwise by isolate)
CLD(lsmeans(lm_set_4.2_WR, ~isolate), alpha=0.1)'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead. isolate lsmean SE df lower.CL upper.CL .group
control 13.3 6.06 49 1.15 25.5 1
201990 16.9 6.06 49 4.68 29.1 1
200715 17.2 6.06 49 5.05 29.4 1
201917 17.6 6.06 49 5.38 29.8 1
200556 18.8 6.06 49 6.62 31.0 1
201888 19.1 6.06 49 6.92 31.3 1
201933 20.3 6.06 49 8.08 32.5 1
201887 20.3 6.06 49 8.15 32.5 1
200533 21.4 6.06 49 9.25 33.6 1
200808 21.7 6.06 49 9.48 33.9 1
201818 22.2 6.06 49 9.98 34.4 1
201895 22.8 6.06 49 10.62 35.0 1
201880 23.4 4.29 49 14.75 32.0 1
201874 24.6 6.06 49 12.42 36.8 1
201870 24.9 10.50 49 3.80 46.0 1
201909 25.7 6.06 49 13.55 37.9 1
201899 27.2 6.06 49 15.02 39.4 1
200444 29.9 6.06 49 17.72 42.1 1
201868 30.7 6.06 49 18.48 42.9 1
201823 31.0 6.06 49 18.82 43.2 1
201910 31.5 6.06 49 19.35 43.7 1
201085 34.8 6.06 49 22.62 47.0 1
201815 40.6 6.06 49 28.42 52.8 1
201084 42.2 6.06 49 30.05 54.4 1
Confidence level used: 0.95
P value adjustment: tukey method for comparing a family of 24 estimates
significance level used: alpha = 0.1 plot(CLD(lsmeans(lm_set_4.2_WR, ~isolate), alpha=0.1), main="All pairwise comparisons for Mean Wet Root Weight", xlab = "Mean Weight (mg)", ylab = "Isolate ID")'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead.
4.2.3. - CLD - Mean Estimate Differences to Control
Compact Letter Display of estimates for Wet Root Mean weight (mg) differences between plants that were inoculated with a mucilage isolates and the non-inoculated control.
plot(CLD(lsmeans(lm_set_4.2_WR, trt.vs.ctrl~isolate,ref=24)$contrasts, alpha=0.1), main="Mean Wet Root Weight Differences of Inoculated Plants vs. Control", xlab = "Difference of Mean Estimates (mg)", ylab = "Contrasts")'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead.No information available to display confidence limits
BCW201084andBCW201815both have mean wet root weight estimates higher than the control and the confidence intervals for these two isolate’s mean estimates do not overlap with those of the control.
4.2.4. Wet Shoot Weight
# linear model of wet shoot data
lm_set_4.2_WS <- lm(mg ~ 1 + isolate, set_4.2_WS)
op = par(mfrow=c(1,2))
plot(lm_set_4.2_WS, which = c(2,3))not plotting observations with leverage one:
19par(op)
4.2.4. - Anova
# assess variance
anova(lm_set_4.2_WS)Analysis of Variance Table
Response: mg
Df Sum Sq Mean Sq F value Pr(>F)
isolate 23 324195 14096 0.7205 0.8018
Residuals 49 958659 19564 4.2.4. - LSMeans - Dunnett
lsm_s4.2.dun.WS <- summary(lsmeans(lm_set_4.2_WS, trt.vs.ctrl ~isolate, ref=24)$contrasts, infer = c(T,T))
write.csv(lsm_s4.2.dun.WS, "./lsmeans_summary_tables/lsm_s4.2.dun.WS.csv")4.2.4. - CLD - (Pairwise by isolate)
CLD(lsmeans(lm_set_4.2_WS, ~isolate), alpha=0.1)'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead. isolate lsmean SE df lower.CL upper.CL .group
201887 206 80.8 49 43.6 368 1
control 251 80.8 49 88.5 413 1
200556 255 80.8 49 93.2 418 1
201874 266 80.8 49 103.4 428 1
201818 273 80.8 49 110.6 435 1
201910 289 80.8 49 126.3 451 1
200715 291 80.8 49 129.0 454 1
200533 314 80.8 49 152.0 477 1
201917 315 80.8 49 152.6 477 1
201888 319 80.8 49 156.5 481 1
201895 331 80.8 49 168.8 493 1
201909 368 80.8 49 206.0 531 1
201933 370 80.8 49 207.5 532 1
201990 374 80.8 49 211.6 536 1
201870 380 139.9 49 99.3 661 1
201880 381 57.1 49 265.8 495 1
201084 384 80.8 49 221.4 546 1
200808 387 80.8 49 224.9 550 1
200444 391 80.8 49 228.9 553 1
201868 408 80.8 49 245.3 570 1
201899 408 80.8 49 245.6 570 1
201823 419 80.8 49 256.6 581 1
201085 444 80.8 49 281.2 606 1
201815 480 80.8 49 317.5 642 1
Confidence level used: 0.95
P value adjustment: tukey method for comparing a family of 24 estimates
significance level used: alpha = 0.1 plot(CLD(lsmeans(lm_set_4.2_WS, ~isolate), alpha=0.1), main="All pairwise comparisons for Mean Wet Shoot Weight", xlab = "Mean Weight (mg)", ylab = "Isolate ID")'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead.
4.2.4. - CLD - Mean Estimate Differences to Control
Compact Letter Display of estimates for Wet Shoot Mean weight (mg) differences between plants that were inoculated with a mucilage isolates and the non-inoculated control.
plot(CLD(lsmeans(lm_set_4.2_WS, trt.vs.ctrl~isolate,ref=24)$contrasts, alpha=0.1), main="Mean Wet Shoot Weight Differences of Inoculated Plants vs. Control", xlab = "Difference of Mean Estimates (mg)", ylab = "Contrasts")'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead.No information available to display confidence limits
There are no positive effects observed based on measurements of wet shoot weight for plants inoculated with isolates in batch 4.2 that have non-overlapping confidence intervals with those of the control.
Batch 4.3.
# analyze set 4.3 data
str(set_4.3)'data.frame': 528 obs. of 4 variables:
$ isolate: chr "200723" "200723" "200723" "200723" ...
$ rep : int 1 1 1 1 1 1 2 2 2 2 ...
$ sample : chr "WS" "WR" "DS" "DR" ...
$ mg : num 376.9 9.3 22.3 0.2 58218.3 ...#subset by sample type
set_4.3_DR <- filter(set_4.3, sample == "DR")
set_4.3_DS <- filter(set_4.3, sample == "DS")
set_4.3_WR <- filter(set_4.3, sample == "WR")
set_4.3_WS <- filter(set_4.3, sample == "WS")Experimental Design Table
| Structure | Factor | Type | # levels |
|---|---|---|---|
| Treatment | Isolate | Qualitative | 30 |
| Design | MS Media Slants | Experimental Unit | 90 |
| Response | Plant Tissue Weight | Observational Unit | 360 |
| Response | Dry Root Weight (DR) | Variable | 90 |
| Response | Dry Shoot Weight (DS) | Variable | 90 |
| Response | Wet Root Weight (WR) | Variable | 90 |
| Response | Wet Shoot Weight (WS) | Variable | 90 |
4.3.1. Dry Root Weight
# linear model of dry root data
lm_set_4.3_DR <- lm(mg ~ 1 + isolate, set_4.3_DR)
op = par(mfrow=c(1,2))
plot(lm_set_4.3_DR, which = c(2,3))
par(op)
4.3.1. - Anova
# assess variance
anova(lm_set_4.3_DR)Analysis of Variance Table
Response: mg
Df Sum Sq Mean Sq F value Pr(>F)
isolate 24 16.393 0.68303 0.714 0.813
Residuals 49 46.872 0.95656 4.3.1. - LSMeans - Dunnett
lsm_s4.3.dun.DR <- summary(lsmeans(lm_set_4.3_DR, trt.vs.ctrl ~isolate, ref=25)$contrasts, infer = c(T,T))
write.csv(lsm_s4.3.dun.DR, "./lsmeans_summary_tables/lsm_s4.3.dun.DR.csv")4.3.1. - CLD - (Pairwise by isolate)
CLD(lsmeans(lm_set_4.3_DR, ~isolate), alpha=0.1)'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead. isolate lsmean SE df lower.CL upper.CL .group
201853 0.667 0.565 49 -0.4681 1.80 1
200634 0.933 0.565 49 -0.2014 2.07 1
201884 0.967 0.565 49 -0.1681 2.10 1
200723 1.200 0.565 49 0.0652 2.33 1
201849 1.267 0.565 49 0.1319 2.40 1
201019 1.333 0.565 49 0.1986 2.47 1
201453 1.333 0.565 49 0.1986 2.47 1
201838 1.367 0.565 49 0.2319 2.50 1
200443 1.367 0.565 49 0.2319 2.50 1
200926 1.433 0.565 49 0.2986 2.57 1
201290 1.700 0.565 49 0.5652 2.83 1
200470 1.733 0.565 49 0.5986 2.87 1
200567 1.733 0.565 49 0.5986 2.87 1
200505 1.733 0.565 49 0.5986 2.87 1
200669 1.800 0.565 49 0.6652 2.93 1
201877 1.833 0.565 49 0.6986 2.97 1
201862 1.867 0.565 49 0.7319 3.00 1
201812 1.867 0.565 49 0.7319 3.00 1
201826 1.900 0.565 49 0.7652 3.03 1
201302 1.933 0.565 49 0.7986 3.07 1
200648 1.950 0.692 49 0.5602 3.34 1
201263 2.067 0.565 49 0.9319 3.20 1
201079 2.167 0.565 49 1.0319 3.30 1
201350 2.567 0.565 49 1.4319 3.70 1
Control 2.700 0.565 49 1.5652 3.83 1
Confidence level used: 0.95
P value adjustment: tukey method for comparing a family of 25 estimates
significance level used: alpha = 0.1 plot(CLD(lsmeans(lm_set_4.3_DR, ~isolate), alpha=0.1), main="All pairwise comparisons for Mean Dry Root Weight", xlab = "Mean Weight (mg)", ylab = "Isolate ID")'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead.
4.3.1. - CLD - Mean Estimate Differences to Control
Compact Letter Display of estimates for Dry Root Mean weight (mg) differences between plants that were inoculated with a mucilage isolates and the non-inoculated control.
plot(CLD(lsmeans(lm_set_4.3_DR, trt.vs.ctrl~isolate,ref=25)$contrasts, alpha=0.1), main="Mean Dry Root Weight Differences of Inoculated Plants vs. Control", xlab = "Difference of Mean Estimates (mg)", ylab = "Contrasts")'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead.No information available to display confidence limits
No significant effect observed for dry root measurements in batch 4.3.
4.3.2. Dry Shoot Weight
# linear model of dry shoot data
lm_set_4.3_DS <- lm(mg ~ 1 + isolate, set_4.3_DS)
op = par(mfrow=c(1,2))
plot(lm_set_4.3_DS, which = c(2,3))
par(op)
4.3.2. - Anova
# assess variance
anova(lm_set_4.3_DS)Analysis of Variance Table
Response: mg
Df Sum Sq Mean Sq F value Pr(>F)
isolate 24 889.38 37.058 0.6891 0.8376
Residuals 49 2634.98 53.775 4.3.2. - LSMeans - Dunnett
lsm_s4.3.dun.DS <- summary(lsmeans(lm_set_4.3_DS, trt.vs.ctrl ~isolate, ref=25)$contrasts, infer = c(T,T))
write.csv(lsm_s4.3.dun.DS, "./lsmeans_summary_tables/lsm_s4.3.dun.DS.csv")4.3.2. - CLD - (Pairwise by isolate)
CLD(lsmeans(lm_set_4.3_DS, ~isolate), alpha=0.1)'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead. isolate lsmean SE df lower.CL upper.CL .group
201849 12.1 4.23 49 3.59 20.6 1
200443 12.2 4.23 49 3.66 20.7 1
200926 12.6 4.23 49 4.09 21.1 1
201853 14.5 4.23 49 6.03 23.0 1
201884 14.7 4.23 49 6.16 23.2 1
201290 15.3 4.23 49 6.76 23.8 1
201812 15.4 4.23 49 6.86 23.9 1
201302 16.6 4.23 49 8.09 25.1 1
200470 16.9 4.23 49 8.39 25.4 1
201019 17.0 4.23 49 8.49 25.5 1
201838 17.0 4.23 49 8.53 25.5 1
200634 17.1 4.23 49 8.59 25.6 1
201877 17.2 4.23 49 8.73 25.7 1
200723 17.6 4.23 49 9.13 26.1 1
Control 17.9 4.23 49 9.39 26.4 1
200567 18.1 4.23 49 9.59 26.6 1
201862 18.2 4.23 49 9.69 26.7 1
200648 18.4 5.19 49 7.98 28.8 1
201826 19.2 4.23 49 10.69 27.7 1
200505 19.2 4.23 49 10.69 27.7 1
201079 21.2 4.23 49 12.73 29.7 1
201453 22.2 4.23 49 13.69 30.7 1
200669 23.2 4.23 49 14.66 31.7 1
201263 23.5 4.23 49 14.96 32.0 1
201350 26.3 4.23 49 17.76 34.8 1
Confidence level used: 0.95
P value adjustment: tukey method for comparing a family of 25 estimates
significance level used: alpha = 0.1 plot(CLD(lsmeans(lm_set_4.3_DS, ~isolate), alpha=0.1), main="All pairwise comparisons for Mean Dry Shoot Weight", xlab = "Mean Weight (mg)", ylab = "Isolate ID")'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead.
4.3.2. - CLD - Mean Estimate Differences to Control
Compact Letter Display of estimates for Dry Shoot Mean weight (mg) differences between plants that were inoculated with a mucilage isolates and the non-inoculated control.
plot(CLD(lsmeans(lm_set_4.3_DS, trt.vs.ctrl~isolate,ref=25)$contrasts, alpha=0.1), main="Mean Dry Shoot Weight Differences of Inoculated Plants vs. Control", xlab = "Difference of Mean Estimates (mg)", ylab = "Contrasts")'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead.No information available to display confidence limits
No significant effect observed for dry shoot measurements in batch 4.3.
4.3.3. Wet Root Weight
# linear model of wet root data
lm_set_4.3_WR <- lm(mg ~ 1 + isolate, set_4.3_WR)
op = par(mfrow=c(1,2))
plot(lm_set_4.3_WR, which = c(2,3))
par(op)
4.3.3. - Anova
# assess variance
anova(lm_set_4.3_WR)Analysis of Variance Table
Response: mg
Df Sum Sq Mean Sq F value Pr(>F)
isolate 24 3098.2 129.09 1.1735 0.3102
Residuals 49 5390.1 110.00 4.3.3. - LSMeans - Dunnett
lsm_s4.3.dun.WR <- summary(lsmeans(lm_set_4.3_WR, trt.vs.ctrl ~isolate, ref=25)$contrasts, infer = c(T,T))
write.csv(lsm_s4.3.dun.WR, "./lsmeans_summary_tables/lsm_s4.3.dun.WR.csv")4.3.3. - CLD - (Pairwise by isolate)
CLD(lsmeans(lm_set_4.3_WR, ~isolate), alpha=0.1)'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead. isolate lsmean SE df lower.CL upper.CL .group
201853 9.27 6.06 49 -2.9020 21.4 1
201884 12.20 6.06 49 0.0313 24.4 1
200634 14.77 6.06 49 2.5980 26.9 1
201453 14.83 6.06 49 2.6646 27.0 1
201849 15.77 6.06 49 3.5980 27.9 1
201019 16.13 6.06 49 3.9646 28.3 1
200926 18.00 6.06 49 5.8313 30.2 1
200723 18.23 6.06 49 6.0646 30.4 1
200443 18.23 6.06 49 6.0646 30.4 1
200505 18.53 6.06 49 6.3646 30.7 1
201838 18.63 6.06 49 6.4646 30.8 1
200567 19.33 6.06 49 7.1646 31.5 1
201290 21.63 6.06 49 9.4646 33.8 1
201826 21.73 6.06 49 9.5646 33.9 1
200470 21.77 6.06 49 9.5980 33.9 1
201862 22.17 6.06 49 9.9980 34.3 1
200669 22.40 6.06 49 10.2313 34.6 1
200648 23.50 7.42 49 8.5964 38.4 1
201302 24.27 6.06 49 12.0980 36.4 1
201812 25.13 6.06 49 12.9646 37.3 1
201263 26.30 6.06 49 14.1313 38.5 1
201877 26.53 6.06 49 14.3646 38.7 1
201350 29.03 6.06 49 16.8646 41.2 1
201079 33.03 6.06 49 20.8646 45.2 1
Control 39.63 6.06 49 27.4646 51.8 1
Confidence level used: 0.95
P value adjustment: tukey method for comparing a family of 25 estimates
significance level used: alpha = 0.1 plot(CLD(lsmeans(lm_set_4.3_WR, ~isolate), alpha=0.1), main="All pairwise comparisons for Mean Wet Root Weight", xlab = "Mean Weight (mg)", ylab = "Isolate ID")'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead.
4.3.3. - CLD - Mean Estimate Differences to Control
Compact Letter Display of estimates for Wet Root Mean weight (mg) differences between plants that were inoculated with a mucilage isolates and the non-inoculated control.
plot(CLD(lsmeans(lm_set_4.3_WR, trt.vs.ctrl~isolate,ref=25)$contrasts, alpha=0.1), main="Mean Wet Root Weight Differences of Inoculated Plants vs. Control", xlab = "Difference of Mean Estimates (mg)", ylab = "Contrasts")'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead.No information available to display confidence limits
No significant positive effect observed for wet root measurements in batch 4.3.
4.3.4. Wet Shoot Weight
# linear model of wet shoot data
lm_set_4.3_WS <- lm(mg ~ 1 + isolate, set_4.3_WS)
op = par(mfrow=c(1,2))
plot(lm_set_4.3_WS, which = c(2,3))
par(op)
4.3.4. - Anova
# assess variance
anova(lm_set_4.3_WS)Analysis of Variance Table
Response: mg
Df Sum Sq Mean Sq F value Pr(>F)
isolate 24 216370 9015.4 0.6651 0.8598
Residuals 49 664198 13555.1 4.3.4. - LSMeans - Dunnett
lsm_s4.3.dun.WS <- summary(lsmeans(lm_set_4.3_WS, trt.vs.ctrl ~isolate, ref=25)$contrasts, infer = c(T,T))
write.csv(lsm_s4.3.dun.WS, "./lsmeans_summary_tables/lsm_s4.3.dun.WS.csv")4.3.4. - CLD - (Pairwise by isolate)
CLD(lsmeans(lm_set_4.3_WS, ~isolate), alpha=0.1)'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead. isolate lsmean SE df lower.CL upper.CL .group
201849 199 67.2 49 63.6 334 1
201853 211 67.2 49 76.3 346 1
200443 212 67.2 49 77.0 347 1
200926 214 67.2 49 78.5 349 1
201884 218 67.2 49 82.9 353 1
201302 246 67.2 49 110.7 381 1
201290 246 67.2 49 111.3 381 1
200634 252 67.2 49 116.6 387 1
201838 256 67.2 49 120.5 391 1
201812 259 67.2 49 123.9 394 1
201019 262 67.2 49 126.9 397 1
200470 273 67.2 49 137.9 408 1
201862 287 67.2 49 151.6 422 1
200723 297 67.2 49 162.2 432 1
Control 303 67.2 49 167.8 438 1
200505 304 67.2 49 169.4 440 1
201877 307 67.2 49 171.6 442 1
201826 316 67.2 49 181.1 451 1
200648 318 82.3 49 152.1 483 1
201453 323 67.2 49 187.7 458 1
200567 323 67.2 49 187.7 458 1
200669 361 67.2 49 225.5 496 1
201079 363 67.2 49 228.1 498 1
201263 365 67.2 49 230.3 500 1
201350 403 67.2 49 267.7 538 1
Confidence level used: 0.95
P value adjustment: tukey method for comparing a family of 25 estimates
significance level used: alpha = 0.1 plot(CLD(lsmeans(lm_set_4.3_WS, ~isolate), alpha=0.1), main="All pairwise comparisons for Mean Wet Shoot Weight", xlab = "Mean Weight (mg)", ylab = "Isolate ID")'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead.
4.3.4. - CLD - Mean Estimate Differences to Control
Compact Letter Display of estimates for Wet Shoot Mean weight (mg) differences between plants that were inoculated with a mucilage isolates and the non-inoculated control.
plot(CLD(lsmeans(lm_set_4.3_WS, trt.vs.ctrl~isolate,ref=25)$contrasts, alpha=0.1), main="Mean Wet Shoot Weight Differences of Inoculated Plants vs. Control", xlab = "Difference of Mean Estimates (mg)", ylab = "Contrasts")'CLD' will be deprecated. Its use is discouraged.
See '? CLD' for an explanation. Use 'pwpp' or 'multcomp::cld' instead.No information available to display confidence limits
No significant positive effect observed for inoculation treatments on wet shoot measurements in batch 4.3.
Summary
The isolates below were each found to have statistically significant differences in mean weight for the specified response variables that were measured, where inclusion in the summary table was contingent upon the confidence intervals for the estimates being non-overlapping with those of the experimental control group (alpha level of 0.1 with correction for multiple testing).
| Isolate List | Batch | Response | Difference from Control (mg) |
|---|---|---|---|
| BCW201858 | 1 | Dry Root | 9.70 |
| BCW200727 | 1 | Dry Shoot | 21.08 |
| BCW200556 | 1 | Wet Root | 95.85 |
| BCW200533 | 1 | Wet Root | 93.01 |
| BCW200810 | 2.1 | Dry Shoot | 21.75 |
| BCW200810 | 2.1 | Wet Root | 60.9 |
| BCW201873 | 2.1 | Wet Root | 70.43 |
| BCW201809 | 2.1 | Wet Root | 62.01 |
| BCW201864 | 2.1 | Wet Root | 65.43 |
| BCW201849 | 2.2 | Dry Root | 5.02 |
| BCW201849 | 2.2 | Dry Shoot | 29.35 |
| BCW201849 | 2.2 | Wet Shoot | 415.22 |
| BCW201881 | 2.3 | Dry Root | 11.94 |
| BCW201900 | 2.4 | Dry Root | 5.63 |
| BCW201900 | 2.4 | Dry Shoot | 25.92 |
| BCW201814 | 2.4 | Dry Shoot | 24.19 |
| BCW201900 | 2.4 | Wet Root | 79.7 |
| BCW201814 | 2.4 | Wet Shoot | 248.28 |
| BCW201900 | 2.4 | Wet Shoot | 397.05 |
| BCW201088 | 3.1 | Dry Root | 3.90 |
| BCW201914 | 3.1 | Dry Shoot | 20.23 |
| BCW201815 | 4.2 | Dry Root | 2.67 |
| BCW201084 | 4.2 | Dry Root | 2.43 |
| BCW201815 | 4.2 | Dry Shoot | 21.10 |
| BCW201084 | 4.2 | Wet Root | 28.90 |
| BCW201815 | 4.2 | Wet Root | 27.27 |
Total Number of isolates with statistically supported positive effects on mean weight: 16 Note: There are several isolates in the first batch that display the trend of highly increased plant biomass, either in shoots or roots, but the confidence intervals overlap with those of the controls based on linear modeling of the data.
N isolates in each batch
unique(set_1$isolate)
[1] "200488" "201859" "201813" "201880" "201236" "201860" "201835" "201085" "201885"
[10] "201887" "201875" "201990" "200726" "201025" "201874" "201836" "201879" "201870"
[19] "201933" "201936" "201840" "200659" "200275" "201889" "201162" "201858" "201851"
[28] "200939" "201910" "200725" "200444" "201917" "201895" "201909" "200715" "201173"
[37] "200661" "201876" "200808" "200533" "201818" "201868" "201815" "200902" "201899"
[46] "201823" "201084" "201245" "201824" "200727" "201888" "201045" "200556" "201856"
[55] "200270" "201153" "200496" "control_W" "control-WO"---
title: "Inoculating Potato Plantlets with Totontepec maize aerial root mucilage isolates"
author: "Shawn Higdon"
output:
  html_notebook:
    toc: yes
    toc_float: yes
  pdf_document:
    toc: yes
---

```{r}
library(lsmeans)
library(tidyverse)

```

# Read in the Datasets
```{r}
set_1 <- read.csv("./all_potato_data/Set1-Table 1.csv", header = T)
set_2.1 <- read.csv("./all_potato_data/Set2_Day1-Table 1.csv", header = T) 
set_2.2 <- read.csv("./all_potato_data/Set2_Day2-Table 1.csv", header = T)
set_2.3 <- read.csv("./all_potato_data/Set2_Day3-Table 1.csv", header = T) 
set_2.4 <- read.csv("./all_potato_data/Set2_Day4-Table 1.csv", header = T) 
set_3.1 <- read.csv("./all_potato_data/Set3_Day1-Table 1.csv", header = T) 
set_3.2 <- read.csv("./all_potato_data/Set3_Day2-Table 1.csv", header = T) 
set_3.3 <- read.csv("./all_potato_data/Set3_Day3-Table 1.csv", header = T) 
set_3.4 <- read.csv("./all_potato_data/Set3_Day4-Table 1.csv", header = T) 
set_3.5 <- read.csv("./all_potato_data/Set3_Day5-Table 1.csv", header = T) 
set_4.1 <- read.csv("./all_potato_data/Set4_Day1-Table 1.csv", header = T) 
set_4.2 <- read.csv("./all_potato_data/Set4_Day2-Table 1.csv", header = T) 
set_4.3 <- read.csv("./all_potato_data/Set4_Day3-Table 1.csv", header = T) 

# combine all datasets
master_set <- rbind(set_1, set_2.1, set_2.2, set_2.3, set_2.4, set_3.1, set_3.2, set_3.3, set_3.4, set_3.5, set_4.1, set_4.2, set_4.3)

```

## Create list of isolates
```{r}
potato_isolate_list <- as_tibble(unique(master_set$isolate)) # grab unique isolate IDs

names(potato_isolate_list) <- "Isolate" # change variable name

str(potato_isolate_list)

potato_isolate_list <- potato_isolate_list %>%
  mutate_if(is.factor, na_if, y = "") # add NA to empty values

potato_isolate_list <- potato_isolate_list %>% 
  filter(grepl("2", Isolate)) # remove rows without '2' prefix (non-isolate rows)

potato_isolate_list$Isolate <- paste0('BCW-', potato_isolate_list$Isolate) # add BCW- prefix

potato_isolate_list <- arrange(potato_isolate_list, Isolate) # arrange in descending order

write_csv(potato_isolate_list, "./R_Output_Files/potato_isolate_list.csv", col_names = T)

```


# Statistical Analysis

## Experimental Design

* Potato plantlets purchased from Canada in bulk were individually transplanted into 1X MS media slants.
* Mucilage Isolates were cultured in Luria's Broth (LB) to an OD600 value of 0.6.
* 100 µL of each inoculum was applied to a plantlet vial, was sealed, and the vials were incubated at 28 ºC for 3 weeks.
* Each inoculation was evaluated in triplicate.
* All Isolates that were incorporated into the whole genome sequencing study were assessed with this assay.
* The number of isolates to be screened led to the full list being assessed in batches:
  * Batch 1
  * Batch 2 (4 groups)
  * Batch 3 (5 groups)
  * Batch 4 (3 groups)

## Linear Modeling

Data measurements were fit to a linear model using the `lm` function of R.
The normality of the data was assessed by visualizing parameters of each linear model in two ways:

* Normal Q-Q plots (Standardized residuals vs. Theoretical Quantiles)
* Scale - Location plots (Squareroot of Standard residuals vs. fitted values)

## Anova

One way analysis of variance was applied to each linear model as a preliminary assessment of each inoculation batch.

## The LSMeans Package

The LSMeans Package was used to calculate mean estimates, confidence intervals, and conduct multiple t-tests. Correction for multiple testing was implemented using either the Dunnett method, or the Tukey method (depending on the type of comparisons), and the adjusted p-values are presented in the outputs.

## Compact Letter Display

Compact Letter Display is a function in R that assigns a common label to each treatment group based on whether or not the confidence intervals of the mean estimations from LSMeans overlap with those of other treatment groups. If two treatment groups have non-overlapping confidence intervals associated with their mean estimates, they are not classified within the same group. This allows higher security in asserting that the mean estimates are truly different at the specified confidence level.  

## Batch 1

```{r}
# analyze set 1 data
str(set_1)

#subset by sample type

set_1_DR <- filter(set_1, sample == "DR")
set_1_DS <- filter(set_1, sample == "DS")
set_1_WR <- filter(set_1, sample == "WR")
set_1_WS <- filter(set_1, sample == "WS")
```

**Experimental Design Table**

| Structure | Factor | Type | # levels |
|-----------|--------|------|----------|
| Treatment | Isolate | Qualitative | 59 |
| Design    | MS Media Slants | Experimental Unit | 177 |
| Response  | Plant Tissue Weight | Observational Unit | 708 |
|Response | Dry Root Weight (DR) | Variable | 177 | 
| Response | Dry Shoot Weight (DS) | Variable | 177 |
| Response | Wet Root Weight (WR) | Variable | 177 | 
| Response | Wet Shoot Weight (WS) | Variable | 177 |

* 57 Isolates
* 2 controls
  * control_W - 100 µL of saline solution
  * control-WO - no added solution

### 1.1. Dry Root Weight

```{r, fig.width=10}
# linear model of dry root data
lm_set_1_DR <- lm(mg ~ 1 + isolate, set_1_DR)
op = par(mfrow=c(1,2))
plot(lm_set_1_DR, which = c(2,3))
par(op)
```

#### 1.1. - Anova

```{r}
anova(lm_set_1_DR)
```

#### 1.1. - LSMeans (Control_W) - Dunnett

```{r}
lsm_s1.dun.DR <- summary(lsmeans(lm_set_1_DR, trt.vs.ctrl ~isolate, ref=45)$contrasts, infer = c(T,T))
write.csv(lsm_s1.dun.DR, "./lsmeans_summary_tables/lsm_s1.dun.DR.csv")
```

#### 1.1. - LSMeans (Control-WO) - Dunnett

```{r}
summary(lsmeans(lm_set_1_DR, trt.vs.ctrl ~isolate, ref=46)$contrasts, infer = c(T,T))
```

#### 1.1 - CLD - (Pairwise by isolate)

```{r}
CLD(lsmeans(lm_set_1_DR, ~isolate), alpha=0.1)
plot(CLD(lsmeans(lm_set_1_DR, ~isolate), alpha=0.1), main="All pairwise comparisons for Mean Dry Root Weight", xlab = "Mean Weight (mg)", ylab = "Isolate ID")
```


#### 1.1. - CLD - Mean Estimate Differences to Control_W

Compact Letter Display of estimates for Dry Root Mean weight (mg) differences between plants that were inoculated with a mucilage isolates and the non-inoculated control (Control_W).

```{r}
plot(CLD(lsmeans(lm_set_1_DR, trt.vs.ctrl~isolate,ref=45)$contrasts, alpha=0.1), main="Mean Dry Root Weight Differences of Inoculated Plants vs. Control_W", xlab = "Difference of Mean Estimates (mg)", ylab = "Contrasts")
```

#### 1.1. - CLD - Mean Estimate Differences to Control-WO

Compact Letter Display of estimates for Dry Root Mean weight (mg) differences between plants that were inoculated with a mucilage isolates and the non-inoculated control (Control-WO).

```{r}
plot(CLD(lsmeans(lm_set_1_DR, trt.vs.ctrl~isolate,ref=46)$contrasts, alpha=0.1), main="Mean Dry Root Weight Differences of Inoculated Plants vs. Control-WO", xlab = "Difference of Mean Estimates (mg)", ylab = "Contrasts")
```

> Based on the means comparison analyses above of the dry root weight measurements for plants of inoculation batch 1, there appears to be one isolate, BCW201858, that has a significant effect on the mean dry root weight. This is the most apparent when comparing to control_W, where the confidence intervals for these mean weight estimates do not overlap [see: 1.1 - CLD - Control_W (Pairwise by isolate)].

### 1.2. Dry Shoot Weight

```{r, fig.width=10}
# linear model of dry shoot data
lm_set_1_DS <- lm(mg ~ 1 + isolate, set_1_DS)
op = par(mfrow=c(1,2))
plot(lm_set_1_DS, which = c(2,3))
par(op)
```

#### 1.2. - Anova

```{r}
anova(lm_set_1_DS)
```

#### 1.2. - LSMeans (Control_W) - Dunnett

```{r}
lsm_s1.dun.DS <- summary(lsmeans(lm_set_1_DS, trt.vs.ctrl ~isolate, ref=45)$contrasts, infer = c(T,T))
write.csv(lsm_s1.dun.DS, "./lsmeans_summary_tables/lsm_s1.dun.DS.csv")
```

#### 1.2. - LSMeans (Control-WO) - Dunnett

```{r}
summary(lsmeans(lm_set_1_DS, trt.vs.ctrl ~isolate, ref=46)$contrasts, infer = c(T,T))
```

#### 1.2 - CLD - (Pairwise by isolate)

```{r}
CLD(lsmeans(lm_set_1_DS, ~isolate), alpha=0.1)
plot(CLD(lsmeans(lm_set_1_DS, ~isolate), alpha=0.1), main="All pairwise comparisons for Mean Dry Shoot Weight", xlab = "Mean Weight (mg)", ylab = "Isolate ID")
```


#### 1.2. - CLD - Mean Estimate Differences to Control_W

Compact Letter Display of estimates for Dry Shoot Mean weight (mg) differences between plants that were inoculated with a mucilage isolates and the non-inoculated control (Control_W).

```{r}
plot(CLD(lsmeans(lm_set_1_DS, trt.vs.ctrl~isolate,ref=45)$contrasts, alpha=0.1), main="Mean Dry Shoot Weight Differences of Inoculated Plants vs. Control_W", xlab = "Difference of Mean Estimates (mg)", ylab = "Contrasts")
```

#### 1.2. - CLD - Mean Estimate Differences to Control-WO

Compact Letter Display of estimates for Dry Shoot Mean weight (mg) differences between plants that were inoculated with a mucilage isolates and the non-inoculated control (Control-WO).

```{r}
plot(CLD(lsmeans(lm_set_1_DS, trt.vs.ctrl~isolate,ref=46)$contrasts, alpha=0.1), main="Mean Dry Shoot Weight Differences of Inoculated Plants vs. Control-WO", xlab = "Difference of Mean Estimates (mg)", ylab = "Contrasts")
```

> BCW200727 has a mean estimate with non-overlaping Confidence intervals to those of the control_W estimate.

### 1.3. Wet Root Weight

```{r, fig.width=10}
# linear model of Wet Root data
lm_set_1_WR <- lm(mg ~ 1 + isolate, set_1_WR)
#op = par(mfrow=c(1,2))
plot(lm_set_1_WR, which = c(2,3))
#par(op)
```

#### 1.3. - Anova

```{r}
anova(lm_set_1_WR)
summary(lm_set_1_WR, infer=c(T,T))
```

#### 1.3. - LSMeans (Control_W)

```{r}
lsm_s1.dun.WR <- summary(lsmeans(lm_set_1_WR, trt.vs.ctrl ~isolate, ref=46)$contrasts, infer = c(T,T))
write.csv(lsm_s1.dun.WR, "./lsmeans_summary_tables/lsm_s1.dun.WR.csv")
```

#### 1.3. - LSMeans (Control-WO)

```{r}
summary(lsmeans(lm_set_1_WR, trt.vs.ctrl ~isolate, ref=46)$contrasts, infer = c(T,T))
```

#### 1.3 - CLD - (Pairwise by isolate)

```{r}
CLD(lsmeans(lm_set_1_WR, ~isolate), alpha=0.1)
plot(CLD(lsmeans(lm_set_1_WR, ~isolate), alpha=0.1), main="All pairwise comparisons for Mean Wet Root Weight", xlab = "Mean Weight (mg)", ylab = "Isolate ID")
```


#### 1.3. - CLD - Mean Estimate Differences to Control_W

Compact Letter Display of estimates for Wet Root Mean weight (mg) differences between plants that were inoculated with a mucilage isolates and the non-inoculated control (Control_W).

```{r}
plot(CLD(lsmeans(lm_set_1_WR, trt.vs.ctrl~isolate,ref=45)$contrasts, alpha=0.1), main="Mean Wet Root Weight Differences of Inoculated Plants vs. Control_W", xlab = "Difference of Mean Estimates (mg)", ylab = "Contrasts")
```

#### 1.3. - CLD - Mean Estimate Differences to Control-WO

Compact Letter Display of estimates for Wet Root Mean weight (mg) differences between plants that were inoculated with a mucilage isolates and the non-inoculated control (Control-WO).

```{r}
plot(CLD(lsmeans(lm_set_1_WR, trt.vs.ctrl~isolate,ref=46)$contrasts, alpha=0.1), main="Mean Wet Root Weight Differences of Inoculated Plants vs. Control-WO", xlab = "Difference of Mean Estimates (mg)", ylab = "Contrasts")
```

> Regarding wet root mass in milligrams, isolate BCW200556 does not exhibit a mean estimate with a CI that overlaps with those of mean estimates from both controls. This implies the effect is significant at the alpha = 0.1 level.

### 1.4. Wet Shoot Weight
```{r, fig.width=10}
# linear model of Wet shoot data
lm_set_1_WS <- lm(mg ~ 1 + isolate, set_1_WS)
op = par(mfrow=c(1,2))
plot(lm_set_1_WS, which = c(2,3))
par(op)
```

#### 1.4. - Anova

```{r}
anova(lm_set_1_WS)
summary(lm_set_1_WS)
```

#### 1.4. - LSMeans (Control_W)

```{r}
lsm_s1.dun.WS <- summary(lsmeans(lm_set_1_WS, trt.vs.ctrl ~isolate, ref=46)$contrasts, infer = c(T,T))
write.csv(lsm_s1.dun.WS, "./lsmeans_summary_tables/lsm_s1.dun.WS.csv")
```

#### 1.4. - LSMeans (Control-WO)

```{r}
summary(lsmeans(lm_set_1_WS, trt.vs.ctrl ~isolate, ref=47)$contrasts, infer = c(T,T))
```

#### 1.4 - CLD - (Pairwise by isolate)

```{r}
CLD(lsmeans(lm_set_1_WS, ~isolate), alpha=0.1)
plot(CLD(lsmeans(lm_set_1_WS, ~isolate), alpha=0.1), main="All pairwise comparisons for Mean Wet Shoot Weight", xlab = "Mean Weight (mg)", ylab = "Isolate ID")
```

#### 1.4. - CLD - Mean Estimate Differences to Control_W

Compact Letter Display of estimates for Wet Shoot Mean weight (mg) differences between plants that were inoculated with a mucilage isolates and the non-inoculated control (Control_W).

```{r}
plot(CLD(lsmeans(lm_set_1_WS, trt.vs.ctrl~isolate,ref=45)$contrasts, alpha=0.1), main="Mean Wet Shoot Weight Differences of Inoculated Plants vs. Control_W", xlab = "Difference of Mean Estimates (mg)", ylab = "Contrasts")
```

#### 1.4. - CLD - Mean Estimate Differences to Control-WO

Compact Letter Display of estimates for Wet Shoot Mean weight (mg) differences between plants that were inoculated with a mucilage isolates and the non-inoculated control (Control-WO).

```{r}
plot(CLD(lsmeans(lm_set_1_WS, trt.vs.ctrl~isolate,ref=46)$contrasts, alpha=0.1), main="Mean Wet Shoot Weight Differences of Inoculated Plants vs. Control-WO", xlab = "Difference of Mean Estimates (mg)", ylab = "Contrasts")
```

> None of the mean estimates for inoculated isolates have non-overlapping confidence intervals with those of non-inoculated controls.

## Batch 2.1

```{r}
# analyze set 2.1 data
str(set_2.1)

#subset by sample type

set_2.1_DR <- filter(set_2.1, sample == "DR")
set_2.1_DS <- filter(set_2.1, sample == "DS")
set_2.1_WR <- filter(set_2.1, sample == "WR")
set_2.1_WS <- filter(set_2.1, sample == "WS")

```

**Experimental Design Table**

| Structure | Factor | Type | # levels |
|-----------|--------|------|----------|
| Treatment | Isolate | Qualitative | 33 |
| Design    | MS Media Slants | Experimental Unit | 99 |
| Response  | Plant Tissue Weight | Observational Unit | 396 |
|Response | Dry Root Weight (DR) | Variable | 99 | 
| Response | Dry Shoot Weight (DS) | Variable | 99 |
| Response | Wet Root Weight (WR) | Variable | 99 | 
| Response | Wet Shoot Weight (WS) | Variable | 99 |


* Four sets of inoculations were setup independently, each with independent controls.
* Each set of isolates was treated as an independent experiment for the analysis.

### 2.1.1. Dry Root Weight

```{r, fig.width=10}
# linear model of dry root data
lm_set_2.1_DR <- lm(mg ~ 1 + isolate, set_2.1_DR)
op = par(mfrow=c(1,2))
plot(lm_set_2.1_DR, which = c(2,3))
par(op)
```

#### 2.1.1. - Anova

```{r}
# assess variance - but the data isn't normal
anova(lm_set_2.1_DR)
```

#### 2.1.1. - LSMeans - Dunnett

```{r}
lsm_s2.1.dun.DR <- summary(lsmeans(lm_set_2.1_DR, trt.vs.ctrl ~isolate, ref=31)$contrasts, infer = c(T,T))
write.csv(lsm_s2.1.dun.DR, "./lsmeans_summary_tables/lsm_s2.1.dun.DR.csv")
```

#### 2.1.1. - CLD - (Pairwise by isolate)

```{r}
CLD(lsmeans(lm_set_2.1_DR, ~isolate), alpha=0.1)
plot(CLD(lsmeans(lm_set_2.1_DR, ~isolate), alpha=0.1), main="All pairwise comparisons for Mean Dry Root Weight", xlab = "Mean Weight (mg)", ylab = "Isolate ID")
```

#### 2.1.1. - CLD - Mean Estimate Differences to Control

Compact Letter Display of estimates for Dry Root Mean weight (mg) differences between plants that were inoculated with a mucilage isolates and the non-inoculated control.

```{r}
plot(CLD(lsmeans(lm_set_2.1_DR, trt.vs.ctrl~isolate,ref=31)$contrasts, alpha=0.1), main="Mean Dry Root Weight Differences of Inoculated Plants vs. Control", xlab = "Difference of Mean Estimates (mg)", ylab = "Contrasts")
```

> Dry root data suggests that isolate BCW201884 significantly increases root weight of the potato plantlets. I am suspecious of this result and need to check the manually recorded datasheets. The data entered for this isolate could have received a typo during data entry (likely based on the triplicate set entered: 3.6, 87.3, 1.7).

### 2.1.2. Dry Shoot Weight

```{r, fig.width=10}
# linear model of dry shoot data
lm_set_2.1_DS <- lm(mg ~ 1 + isolate, set_2.1_DS)
op = par(mfrow=c(1,2))
plot(lm_set_2.1_DS, which = c(2,3))
par(op)
```

#### 2.1.2. - Anova

```{r}
# assess variance
anova(lm_set_2.1_DS)
```

#### 2.1.2. - LSMeans - Dunnett

```{r}
lsm_s2.1.dun.DS <- summary(lsmeans(lm_set_2.1_DS, trt.vs.ctrl ~isolate, ref=31)$contrasts, infer = c(T,T))
write.csv(lsm_s2.1.dun.DS, "./lsmeans_summary_tables/lsm_s2.1.dun.DS.csv")
```

#### 2.1.2 - CLD - (Pairwise by isolate)

```{r}
CLD(lsmeans(lm_set_2.1_DS, ~isolate), alpha=0.1)
plot(CLD(lsmeans(lm_set_2.1_DS, ~isolate), alpha=0.1), main="All pairwise comparisons for Mean Dry Shoot Weight", xlab = "Mean Weight (mg)", ylab = "Isolate ID")
```

#### 2.1.2. - CLD - Mean Estimate Differences to Control

Compact Letter Display of estimates for Dry Shoot Mean weight (mg) differences between plants that were inoculated with a mucilage isolates and the non-inoculated control.

```{r}
plot(CLD(lsmeans(lm_set_2.1_DS, trt.vs.ctrl~isolate,ref=31)$contrasts, alpha=0.1), main="Mean Dry Shoot Weight Differences of Inoculated Plants vs. Control", xlab = "Difference of Mean Estimates (mg)", ylab = "Contrasts")
```

> The difference between the control and BCW200810 is statistically significant at an alpha level of 0.1.

### 2.1.3. Wet Root Weight

```{r, fig.width=10}
# linear model of wet root data
lm_set_2.1_WR <- lm(mg ~ 1 + isolate, set_2.1_WR)
op = par(mfrow=c(1,2))
plot(lm_set_2.1_WR, which = c(2,3))
par(op)
```

#### 2.1.3. - Anova

```{r}

#assess variance
anova(lm_set_2.1_WR)
```

#### 2.1.3. - LSMeans - Dunnett

```{r}
lsm_s2.1.dun.WR <- summary(lsmeans(lm_set_2.1_WR, trt.vs.ctrl ~isolate, ref=31)$contrasts, infer = c(T,T))
write.csv(lsm_s2.1.dun.WR, "./lsmeans_summary_tables/lsm_s2.1.dun.WR.csv")
```

#### 2.1.3. - CLD - (Pairwise by isolate)

```{r}
CLD(lsmeans(lm_set_2.1_WR, ~isolate), alpha=0.1)
plot(CLD(lsmeans(lm_set_2.1_WR, ~isolate), alpha=0.1), main="All pairwise comparisons for Mean Wet Root Weight", xlab = "Mean Weight (mg)", ylab = "Isolate ID")
```

#### 2.1.3. - CLD - Mean Estimate Differences to Control

Compact Letter Display of estimates for Wet Root Mean weight (mg) differences between plants that were inoculated with a mucilage isolates and the non-inoculated control.

```{r}
plot(CLD(lsmeans(lm_set_2.1_WR, trt.vs.ctrl~isolate,ref=31)$contrasts, alpha=0.1), main="Mean Wet Root Weight Differences of Inoculated Plants vs. Control", xlab = "Difference of Mean Estimates (mg)", ylab = "Contrasts")
```

> Based on the modeling and the plot of mean wet root weights, the cluster of four isolates with mean wet root differences around 60 mg appear to be strong candidates. Isolates BCW201873, BCW200810, BCW201809, BCW201864.

### 2.1.4. Wet Shoot Weight

```{r, fig.width=10}
# linear model of wet shoot data
lm_set_2.1_WS <- lm(mg ~ 1 + isolate, set_2.1_WS)
op = par(mfrow=c(1,2))
plot(lm_set_2.1_WS, which = c(2,3))
par(op)
```

#### 2.1.4. - Anova

```{r}

# assess variance
anova(lm_set_2.1_WS)
```

#### 2.1.4. - LSMeans - Dunnett

```{r}
lsm_s2.1.dun.WS <- summary(lsmeans(lm_set_2.1_WS, trt.vs.ctrl ~isolate, ref=31)$contrasts, infer = c(T,T))
write.csv(lsm_s2.1.dun.WS, "./lsmeans_summary_tables/lsm_s2.1.dun.WS.csv")
```

#### 2.1.4. - CLD - (Pairwise by isolate)

```{r}
CLD(lsmeans(lm_set_2.1_DS, ~isolate), alpha=0.1)
plot(CLD(lsmeans(lm_set_2.1_WS, ~isolate), alpha=0.1), main="All pairwise comparisons for Mean Dry Shoot Weight", xlab = "Mean Weight (mg)", ylab = "Isolate ID")
```

#### 2.1.4. - CLD - Mean Estimate Differences to Control

Compact Letter Display of estimates for Dry Shoot Mean weight (mg) differences between plants that were inoculated with a mucilage isolates and the non-inoculated control.

```{r}
plot(CLD(lsmeans(lm_set_2.1_DS, trt.vs.ctrl~isolate,ref=31)$contrasts, alpha=0.1), main="Mean Dry Shoot Weight Differences of Inoculated Plants vs. Control", xlab = "Difference of Mean Estimates (mg)", ylab = "Contrasts")
```

> The mean wet shoot weight for BCW200810 is significantly different from that of the control at an alpha level of 0.1

## Batch 2.2

```{r}
# analyze set 2.2 data
str(set_2.2)

#subset by sample type

set_2.2_DR <- filter(set_2.2, sample == "DR")
set_2.2_DS <- filter(set_2.2, sample == "DS")
set_2.2_WR <- filter(set_2.2, sample == "WR")
set_2.2_WS <- filter(set_2.2, sample == "WS")
```

**Experimental Design Table**

| Structure | Factor | Type | # levels |
|-----------|--------|------|----------|
| Treatment | Isolate | Qualitative | 33 |
| Design    | MS Media Slants | Experimental Unit | 99 |
| Response  | Plant Tissue Weight | Observational Unit | 396 |
|Response | Dry Root Weight (DR) | Variable | 99 | 
| Response | Dry Shoot Weight (DS) | Variable | 99 |
| Response | Wet Root Weight (WR) | Variable | 99 | 
| Response | Wet Shoot Weight (WS) | Variable | 99 |

### 2.2.1. Dry Root Weight 

```{r, fig.width=10}
# linear model of dry root data
lm_set_2.2_DR <- lm(mg ~ 1 + isolate, set_2.2_DR)
op = par(mfrow=c(1,2))
plot(lm_set_2.2_DR, which = c(2,3))
par(op)
```

#### 2.2.1. - Anova

```{r}
# assess variance - but the data isn't normal
anova(lm_set_2.2_DR)
```

#### 2.2.1. - LSMeans - Dunnett

```{r}
lsm_s2.2.dun.DR <- summary(lsmeans(lm_set_2.2_DR, trt.vs.ctrl ~isolate, ref=32)$contrasts, infer = c(T,T))
write.csv(lsm_s2.2.dun.DR, "./lsmeans_summary_tables/lsm_s2.2.dun.DR.csv")
```

#### 2.2.1. - CLD - (Pairwise by isolate)

```{r}
CLD(lsmeans(lm_set_2.2_DR, ~isolate), alpha=0.1)
plot(CLD(lsmeans(lm_set_2.2_DR, ~isolate), alpha=0.1), main="All pairwise comparisons for Mean Dry Root Weight", xlab = "Mean Weight (mg)", ylab = "Isolate ID")
```

#### 2.2.1. - CLD - Mean Estimate Differences to Control

Compact Letter Display of estimates for Dry Root Mean weight (mg) differences between plants that were inoculated with a mucilage isolates and the non-inoculated control.

```{r}
plot(CLD(lsmeans(lm_set_2.2_DR, trt.vs.ctrl~isolate,ref=32)$contrasts, alpha=0.1), main="Mean Dry Root Weight Differences of Inoculated Plants vs. Control", xlab = "Difference of Mean Estimates (mg)", ylab = "Contrasts")
```

> BCW201849 has a dry root weight that is significantly higher than the control.

### 2.2.2. Dry Shoot Weight

```{r, fig.width=10}
# linear model of dry shoot data
lm_set_2.2_DS <- lm(mg ~ 1 + isolate, set_2.2_DS)
op = par(mfrow=c(1,2))
plot(lm_set_2.2_DS, which = c(2,3))
par(op)
```

#### 2.2.2. - Anova

```{r}
# assess variance
anova(lm_set_2.2_DS)
```

#### 2.2.2. - LSMeans - Dunnett

```{r}
lsm_s2.2.dun.DS <- summary(lsmeans(lm_set_2.2_DS, trt.vs.ctrl ~isolate, ref=32)$contrasts, infer = c(T,T))
write.csv(lsm_s2.2.dun.DS, "./lsmeans_summary_tables/lsm_s2.2.dun.DS.csv")
```

#### 2.2.2. - CLD - (Pairwise by isolate)

```{r}
CLD(lsmeans(lm_set_2.2_DS, ~isolate), alpha=0.1)
plot(CLD(lsmeans(lm_set_2.2_DS, ~isolate), alpha=0.1), main="All pairwise comparisons for Mean Dry Shoot Weight", xlab = "Mean Weight (mg)", ylab = "Isolate ID")
```

#### 2.2.2. - CLD - Mean Estimate Differences to Control

Compact Letter Display of estimates for Dry Shoot Mean weight (mg) differences between plants that were inoculated with a mucilage isolates and the non-inoculated control.

```{r}
plot(CLD(lsmeans(lm_set_2.2_DS, trt.vs.ctrl~isolate,ref=32)$contrasts, alpha=0.1), main="Mean Dry Shoot Weight Differences of Inoculated Plants vs. Control", xlab = "Difference of Mean Estimates (mg)", ylab = "Contrasts")
```

> BCW201849 also has a mean dry shoot weight that is statistically significant from that of the control at an alpha level of 0.1.

### 2.2.3. Wet Root Weight

```{r, fig.width=10}
# linear model of wet root data
lm_set_2.2_WR <- lm(mg ~ 1 + isolate, set_2.2_WR)
op = par(mfrow=c(1,2))
plot(lm_set_2.2_WR, which = c(2,3))
par(op)
```

#### 2.2.3. - Anova

```{r}
# assess variance
anova(lm_set_2.2_WR)
```

#### 2.2.3. - LSMeans - Dunnett

```{r}
lsm_s2.2.dun.WR <- summary(lsmeans(lm_set_2.2_WR, trt.vs.ctrl ~isolate, ref=32)$contrasts, infer = c(T,T))
write.csv(lsm_s2.2.dun.WR, "./lsmeans_summary_tables/lsm_s2.2.dun.WR.csv")
```

#### 2.2.3. - CLD - (Pairwise by isolate)

```{r}
CLD(lsmeans(lm_set_2.2_WR, ~isolate), alpha=0.1)
plot(CLD(lsmeans(lm_set_2.2_WR, ~isolate), alpha=0.1), main="All pairwise comparisons for Mean Wet Root Weight", xlab = "Mean Weight (mg)", ylab = "Isolate ID")
```

#### 2.2.3. - CLD - Mean Estimate Differences to Control

Compact Letter Display of estimates for Wet Root Mean weight (mg) differences between plants that were inoculated with a mucilage isolates and the non-inoculated control.

```{r}
plot(CLD(lsmeans(lm_set_2.2_WR, trt.vs.ctrl~isolate,ref=31)$contrasts, alpha=0.1), main="Mean Wet Root Weight Differences of Inoculated Plants vs. Control", xlab = "Difference of Mean Estimates (mg)", ylab = "Contrasts")
```

> None of the isolates have non-overlapping confidence intervals for their wet root weight mean esimates.

### 2.2.4. Wet Shoot Weight

```{r, fig.width=10}
# linear model of wet shoot data
lm_set_2.2_WS <- lm(mg ~ 1 + isolate, set_2.2_WS)
op = par(mfrow=c(1,2))
plot(lm_set_2.2_WS, which = c(2,3))
par(op)
```

#### 2.2.4. - Anova

```{r}
# assess variance
anova(lm_set_2.2_WS)
```

#### 2.2.4. - LSMeans - Dunnett

```{r}
lsm_s2.2.dun.WS <- summary(lsmeans(lm_set_2.2_WS, trt.vs.ctrl ~isolate, ref=32)$contrasts, infer = c(T,T))
write.csv(lsm_s2.2.dun.WS, "./lsmeans_summary_tables/lsm_s2.2.dun.WS.csv")
```

#### 2.2.4. - CLD - (Pairwise by isolate)

```{r}
CLD(lsmeans(lm_set_2.2_WS, ~isolate), alpha=0.1)
plot(CLD(lsmeans(lm_set_2.2_WS, ~isolate), alpha=0.1), main="All pairwise comparisons for Mean Wet Shoot Weight", xlab = "Mean Weight (mg)", ylab = "Isolate ID")
```

#### 2.2.4. - CLD - Mean Estimate Differences to Control

Compact Letter Display of estimates for Wet Shoot Mean weight (mg) differences between plants that were inoculated with a mucilage isolates and the non-inoculated control.

```{r}
plot(CLD(lsmeans(lm_set_2.2_WS, trt.vs.ctrl~isolate,ref=32)$contrasts, alpha=0.1), main="Mean Wet Shoot Weight Differences of Inoculated Plants vs. Control", xlab = "Difference of Mean Estimates (mg)", ylab = "Contrasts")
```

> BCW201849 has a mean wet shoot weight that is statistically significant from that of the control at an alpha level of 0.1

## Batch 2.3

```{r}
# analyze set 2.3 data
str(set_2.3)

#subset by sample type

set_2.3_DR <- filter(set_2.3, sample == "DR")
set_2.3_DS <- filter(set_2.3, sample == "DS")
set_2.3_WR <- filter(set_2.3, sample == "WR")
set_2.3_WS <- filter(set_2.3, sample == "WS")
```

**Experimental Design Table**
  
  | Structure | Factor | Type | # levels |
  |-----------|--------|------|----------|
  | Treatment | Isolate | Qualitative | 29 |
  | Design    | MS Media Slants | Experimental Unit | 87 |
  | Response  | Plant Tissue Weight | Observational Unit | 348 |
  |Response | Dry Root Weight (DR) | Variable | 87 | 
  | Response | Dry Shoot Weight (DS) | Variable | 87 |
  | Response | Wet Root Weight (WR) | Variable | 87 | 
  | Response | Wet Shoot Weight (WS) | Variable | 87 |

### 2.3.1. Dry Root Weight

```{r, fig.width=10}
# linear model of dry root data
lm_set_2.3_DR <- lm(mg ~ 1 + isolate, set_2.3_DR)
op = par(mfrow=c(1,2))
plot(lm_set_2.3_DR, which = c(2,3))
par(op)
```

#### 2.3.1. - Anova

```{r}
# assess variance 
anova(lm_set_2.3_DR)
```

> There appears to be no significant differences among the fitted values based on the anova.

#### 2.3.1. - LSMeans - Dunnett

```{r}
lsm_s2.3.dun.DR <- summary(lsmeans(lm_set_2.3_DR, trt.vs.ctrl ~isolate, ref=26)$contrasts, infer = c(T,T))
write.csv(lsm_s2.3.dun.DR, "./lsmeans_summary_tables/lsm_s2.3.dun.DR.csv")
```

#### 2.3.1. - CLD - (Pairwise by isolate)

```{r}
CLD(lsmeans(lm_set_2.3_DR, ~isolate), alpha=0.1)
plot(CLD(lsmeans(lm_set_2.3_DR, ~isolate), alpha=0.1), main="All pairwise comparisons for Mean Dry Root Weight", xlab = "Mean Weight (mg)", ylab = "Isolate ID")
```

#### 2.3.1. - CLD - Mean Estimate Differences to Control

Compact Letter Display of estimates for Dry Root Mean weight (mg) differences between plants that were inoculated with a mucilage isolates and the non-inoculated control.

```{r}
plot(CLD(lsmeans(lm_set_2.3_DR, trt.vs.ctrl~isolate,ref=26)$contrasts, alpha=0.1), main="Mean Dry Root Weight Differences of Inoculated Plants vs. Control", xlab = "Difference of Mean Estimates (mg)", ylab = "Contrasts")
```

> BCW201881 has a higher mean estimate for dry root weight than the control with non-overlapping confidence intervals.

### 2.3.2. Dry Shoot Weight

```{r, fig.width=10}
# linear model of dry shoot data
lm_set_2.3_DS <- lm(mg ~ 1 + isolate, set_2.3_DS)
op = par(mfrow=c(1,2))
plot(lm_set_2.3_DS, which = c(2,3))
par(op)
```

#### 2.3.2. - Anova
```{r}
# assess variance
anova(lm_set_2.3_DS)
```

#### 2.3.2. - LSMeans - Dunnett

```{r}
lsm_s2.3.dun.DS <- summary(lsmeans(lm_set_2.3_DS, trt.vs.ctrl ~isolate, ref=26)$contrasts, infer = c(T,T))
write.csv(lsm_s2.3.dun.DS, "./lsmeans_summary_tables/lsm_s2.3.dun.DS.csv")
```

#### 2.3.2. - CLD - (Pairwise by isolate)

```{r}
CLD(lsmeans(lm_set_2.3_DS, ~isolate), alpha=0.1)
plot(CLD(lsmeans(lm_set_2.3_DS, ~isolate), alpha=0.1), main="All pairwise comparisons for Mean Dry Shoot Weight", xlab = "Mean Weight (mg)", ylab = "Isolate ID")
```

#### 2.3.2. - CLD - Mean Estimate Differences to Control

Compact Letter Display of estimates for Dry Shoot Mean weight (mg) differences between plants that were inoculated with a mucilage isolates and the non-inoculated control.

```{r}
plot(CLD(lsmeans(lm_set_2.3_DS, trt.vs.ctrl~isolate,ref=26)$contrasts, alpha=0.1), main="Mean Dry Shoot Weight Differences of Inoculated Plants vs. Control", xlab = "Difference of Mean Estimates (mg)", ylab = "Contrasts")
```

> All of the isolates have means with confidence intervals that overlap with that of the control for set 2.3

### 2.3.3. Wet Root Weight

```{r, fig.width=10}
# linear model of wet root data
lm_set_2.3_WR <- lm(mg ~ 1 + isolate, set_2.3_WR)
op = par(mfrow=c(1,2))
plot(lm_set_2.3_WR, which = c(2,3))
par(op)
```

#### 2.3.3 - Anova

```{r}

# assess variance
anova(lm_set_2.3_WR)
```

#### 2.3.3. - LSMeans - Dunnett

```{r}
lsm_s2.3.dun.WR <- summary(lsmeans(lm_set_2.3_WR, trt.vs.ctrl ~isolate, ref=26)$contrasts, infer = c(T,T))
write.csv(lsm_s2.3.dun.WR, "./lsmeans_summary_tables/lsm_s2.3.dun.WR.csv")
```

#### 2.3.3. - CLD - (Pairwise by isolate)

```{r}
CLD(lsmeans(lm_set_2.3_WR, ~isolate), alpha=0.1)
plot(CLD(lsmeans(lm_set_2.3_WR, ~isolate), alpha=0.1), main="All pairwise comparisons for Mean Wet Root Weight", xlab = "Mean Weight (mg)", ylab = "Isolate ID")
```

#### 2.3.3. - CLD - Mean Estimate Differences to Control

Compact Letter Display of estimates for Wet Root Mean weight (mg) differences between plants that were inoculated with a mucilage isolates and the non-inoculated control.

```{r}
plot(CLD(lsmeans(lm_set_2.3_WR, trt.vs.ctrl~isolate,ref=26)$contrasts, alpha=0.1), main="Mean Wet Root Weight Differences of Inoculated Plants vs. Control", xlab = "Difference of Mean Estimates (mg)", ylab = "Contrasts")
```


> Wet Root data shows all isolate mean estimates have Confidence Intervals that overlap.

### 2.3.4. Wet Shoot Weight

```{r, fig.width=10}
# linear model of Wet shoot data
lm_set_2.3_WS <- lm(mg ~ 1 + isolate, set_2.3_WS)
op = par(mfrow=c(1,2))
plot(lm_set_2.3_WS, which = c(2,3))
par(op)
```

#### 2.3.4. - Anova

```{r}
# assess variance
anova(lm_set_2.3_WS)
```

> Anova shows similar result to dry shoot.

#### 2.3.4. - LSMeans - Dunnett

```{r}
lsm_s2.3.dun.WS <- summary(lsmeans(lm_set_2.3_WS, trt.vs.ctrl ~isolate, ref=26)$contrasts, infer = c(T,T))
write.csv(lsm_s2.3.dun.WS, "./lsmeans_summary_tables/lsm_s2.3.dun.WS.csv")
```

#### 2.3.4. - CLD - (Pairwise by isolate)

```{r}
CLD(lsmeans(lm_set_2.3_DS, ~isolate), alpha=0.1)
plot(CLD(lsmeans(lm_set_2.3_DS, ~isolate), alpha=0.1), main="All pairwise comparisons for Mean Wet Shoot Weight", xlab = "Mean Weight (mg)", ylab = "Isolate ID")
```

#### 2.3.4. - CLD - Mean Estimate Differences to Control

Compact Letter Display of estimates for Wet Shoot Mean weight (mg) differences between plants that were inoculated with a mucilage isolates and the non-inoculated control.

```{r}
plot(CLD(lsmeans(lm_set_2.3_DS, trt.vs.ctrl~isolate,ref=26)$contrasts, alpha=0.1), main="Mean Wet Shoot Weight Differences of Inoculated Plants vs. Control", xlab = "Difference of Mean Estimates (mg)", ylab = "Contrasts")
```

> All isolates means and CI's overlap with the control.

## Batch 2.4

```{r}
# analyze set 2.4 data
str(set_2.4)

#subset by sample type

set_2.4_DR <- filter(set_2.4, sample == "DR")
set_2.4_DS <- filter(set_2.4, sample == "DS")
set_2.4_WR <- filter(set_2.4, sample == "WR")
set_2.4_WS <- filter(set_2.4, sample == "WS")
```

**Experimental Design Table**
  
  | Structure | Factor | Type | # levels |
  |-----------|--------|------|----------|
  | Treatment | Isolate | Qualitative | 37 |
  | Design    | MS Media Slants | Experimental Unit | 111 |
  | Response  | Plant Tissue Weight | Observational Unit | 444 |
  |Response | Dry Root Weight (DR) | Variable | 111 | 
  | Response | Dry Shoot Weight (DS) | Variable | 111 |
  | Response | Wet Root Weight (WR) | Variable | 111 | 
  | Response | Wet Shoot Weight (WS) | Variable | 111 |

### 2.4.1. Dry Root Weight

```{r, fig.width=10}
# linear model of dry root data for set 2.4
lm_set_2.4_DR <- lm(mg ~ 1 + isolate, set_2.4_DR)
op = par(mfrow=c(1,2))
plot(lm_set_2.4_DR, which = c(2,3))
par(op)
```

#### 2.4.1. - Anova

```{r}
# assess variance
anova(lm_set_2.4_DR)
```

#### 2.4.1. - LSMeans - Dunnett

```{r}
lsm_s2.4.dun.DR <- summary(lsmeans(lm_set_2.4_DR, trt.vs.ctrl ~isolate, ref=36)$contrasts, infer = c(T,T))
write.csv(lsm_s2.4.dun.DR, "./lsmeans_summary_tables/lsm_s2.4.dun.DR.csv")
```

#### 2.4.1. - CLD - (Pairwise by isolate)

```{r}
CLD(lsmeans(lm_set_2.4_DR, ~isolate), alpha=0.1)
plot(CLD(lsmeans(lm_set_2.4_DR, ~isolate), alpha=0.1), main="All pairwise comparisons for Mean Dry Root Weight", xlab = "Mean Weight (mg)", ylab = "Isolate ID")
```

#### 2.4.1. - CLD - Mean Estimate Differences to Control

Compact Letter Display of estimates for Dry Root Mean weight (mg) differences between plants that were inoculated with a mucilage isolates and the non-inoculated control.

```{r}
plot(CLD(lsmeans(lm_set_2.4_DR, trt.vs.ctrl~isolate,ref=36)$contrasts, alpha=0.1), main="Mean Dry Root Weight Differences of Inoculated Plants vs. Control", xlab = "Difference of Mean Estimates (mg)", ylab = "Contrasts")
```

> BCW201900 has a mean dry root weight with non-overlapping confidence intervals relative to those of the control, when compared at an alpha level of 0.1. The mean of 8.63 mg is nearly three times higher than that of the control, which is 3.0 mg.

### 2.4.2. Dry Shoot Weight

```{r, fig.width=10}
# linear model of dry shoot data from set 2.4
lm_set_2.4_DS <- lm(mg ~ 1 + isolate, set_2.4_DS)
op = par(mfrow=c(1,2))
plot(lm_set_2.4_DS, which = c(2,3))
par(op)
```

#### 2.4.2. - Anova

```{r}
# assess variance
anova(lm_set_2.4_DS)
```

#### 2.4.2. - LSMeans - Dunnett

```{r}
lsm_s2.4.dun.DS <- summary(lsmeans(lm_set_2.4_DS, trt.vs.ctrl ~isolate, ref=36)$contrasts, infer = c(T,T))
write.csv(lsm_s2.4.dun.DS, "./lsmeans_summary_tables/lsm_s2.4.dun.DS.csv")
```

#### 2.4.2. - CLD - (Pairwise by isolate)

```{r}
CLD(lsmeans(lm_set_2.4_DS, ~isolate), alpha=0.1)
plot(CLD(lsmeans(lm_set_2.4_DS, ~isolate), alpha=0.1), main="All pairwise comparisons for Mean Dry Shoot Weight", xlab = "Mean Weight (mg)", ylab = "Isolate ID")
```

#### 2.4.2. - CLD - Mean Estimate Differences to Control

Compact Letter Display of estimates for Dry Shoot Mean weight (mg) differences between plants that were inoculated with a mucilage isolates and the non-inoculated control.

```{r}
plot(CLD(lsmeans(lm_set_2.4_DS, trt.vs.ctrl~isolate,ref=36)$contrasts, alpha=0.1), main="Mean Dry Shoot Weight Differences of Inoculated Plants vs. Control", xlab = "Difference of Mean Estimates (mg)", ylab = "Contrasts")
```

> BCW201814 and BCW201900 both have means with confidence intervals that do not overlap with the mean of the control for isolate set 2.4 (at an alpha level of 0.1)

### 2.4.3. Wet Root Weight

```{r, fig.width=10}
# linear model of Wet Root data from set 2.4
lm_set_2.4_WR <- lm(mg ~ 1 + isolate, set_2.4_WR)
op = par(mfrow=c(1,2))
plot(lm_set_2.4_WR, which = c(2,3))
par(op)
```

#### 2.4.3. - Anova

```{r}
# assess variance
anova(lm_set_2.4_WR)
```

#### 2.4.3. - LSMeans - Dunnett

```{r}
lsm_s2.4.dun.WR <- summary(lsmeans(lm_set_2.4_WR, trt.vs.ctrl ~isolate, ref=36)$contrasts, infer = c(T,T))
write.csv(lsm_s2.4.dun.WR, "./lsmeans_summary_tables/lsm_s2.4.dun.WR.csv")
```

#### 2.4.3. - CLD - (Pairwise by isolate)

```{r}
CLD(lsmeans(lm_set_2.4_WR, ~isolate), alpha=0.1)
plot(CLD(lsmeans(lm_set_2.4_WR, ~isolate), alpha=0.1), main="All pairwise comparisons for Mean Wet Root Weight", xlab = "Mean Weight (mg)", ylab = "Isolate ID")
```

#### 2.4.3. - CLD - Mean Estimate Differences to Control

Compact Letter Display of estimates for Wet Root Mean weight (mg) differences between plants that were inoculated with a mucilage isolates and the non-inoculated control.

```{r}
plot(CLD(lsmeans(lm_set_2.4_WR, trt.vs.ctrl~isolate,ref=36)$contrasts, alpha=0.1), main="Mean Wet Root Weight Differences of Inoculated Plants vs. Control", xlab = "Difference of Mean Estimates (mg)", ylab = "Contrasts")
```

> BCW201900 wet root mean and associated CI do not overlap with those of the control, and the mean is three times that of the control.

### 2.4.4. Wet Shoot Weight

```{r, fig.width=10}
# linear model of Wet Shoot data from set 2.4
lm_set_2.4_WS <- lm(mg ~ 1 + isolate, set_2.4_WS)
op = par(mfrow=c(1,2))
plot(lm_set_2.4_WS, which = c(2,3))
par(op)
```

#### 2.4.4. - Anova

```{r}
# assess variance
anova(lm_set_2.4_WS)
```

#### 2.4.4. - LSMeans - Dunnett

```{r}
lsm_s2.4.dun.WS <- summary(lsmeans(lm_set_2.4_WS, trt.vs.ctrl ~isolate, ref=36)$contrasts, infer = c(T,T))
write.csv(lsm_s2.4.dun.WS, "./lsmeans_summary_tables/lsm_s2.4.dun.WS.csv")
```

#### 2.4.4. - CLD - (Pairwise by isolate)

```{r}
CLD(lsmeans(lm_set_2.4_WS, ~isolate), alpha=0.1)
plot(CLD(lsmeans(lm_set_2.4_WS, ~isolate), alpha=0.1), main="All pairwise comparisons for Mean Wet Shoot Weight", xlab = "Mean Weight (mg)", ylab = "Isolate ID")
```

#### 2.4.4. - CLD - Mean Estimate Differences to Control

Compact Letter Display of estimates for Wet Shoot Mean weight (mg) differences between plants that were inoculated with a mucilage isolates and the non-inoculated control.

```{r}
plot(CLD(lsmeans(lm_set_2.4_WS, trt.vs.ctrl~isolate,ref=36)$contrasts, alpha=0.1), main="Mean Wet Shoot Weight Differences of Inoculated Plants vs. Control", xlab = "Difference of Mean Estimates (mg)", ylab = "Contrasts")
```


> BCW201900 and BCW201814 are the only isolates that have mean wet shoot weight estimates with confidence intervals that do not overlap with those of the control.

## Batch 3.1

```{r}
# analyze set 3.1 data
str(set_3.1)

#subset by sample type

set_3.1_DR <- filter(set_3.1, sample == "DR")
set_3.1_DS <- filter(set_3.1, sample == "DS")
set_3.1_WR <- filter(set_3.1, sample == "WR")
set_3.1_WS <- filter(set_3.1, sample == "WS")

```

**Experimental Design Table**
  
  | Structure | Factor | Type | # levels |
  |-----------|--------|------|----------|
  | Treatment | Isolate | Qualitative | 38 |
  | Design    | MS Media Slants | Experimental Unit | 114 |
  | Response  | Plant Tissue Weight | Observational Unit | 456 |
  |Response | Dry Root Weight (DR) | Variable | 114 | 
  | Response | Dry Shoot Weight (DS) | Variable | 114 |
  | Response | Wet Root Weight (WR) | Variable | 114 | 
  | Response | Wet Shoot Weight (WS) | Variable | 114 |

### 3.1.1. Dry Root Weight

```{r, fig.width=10}
# linear model of dry root data
lm_set_3.1_DR <- lm(mg ~ 1 + isolate, set_3.1_DR)
op = par(mfrow=c(1,2))
plot(lm_set_3.1_DR, which = c(2,3))
par(op)
```

#### 3.1.1. - Anova

```{r}
# assess variance
anova(lm_set_3.1_DR)
```

#### 3.1.1. - LSMeans - Dunnett

```{r}
lsm_s3.1.dun.DR <- summary(lsmeans(lm_set_3.1_DR, trt.vs.ctrl ~isolate, ref=37)$contrasts, infer = c(T,T))
write.csv(lsm_s3.1.dun.DR, "./lsmeans_summary_tables/lsm_s3.1.dun.DR.csv")
```

#### 3.1.1. - CLD - (Pairwise by isolate)

```{r}
CLD(lsmeans(lm_set_3.1_DR, ~isolate), alpha=0.1)
plot(CLD(lsmeans(lm_set_3.1_DR, ~isolate), alpha=0.1), main="All pairwise comparisons for Mean Dry Root Weight", xlab = "Mean Weight (mg)", ylab = "Isolate ID")
```

#### 3.1.1. - CLD - Mean Estimate Differences to Control

Compact Letter Display of estimates for Dry Root Mean weight (mg) differences between plants that were inoculated with a mucilage isolates and the non-inoculated control.

```{r}
plot(CLD(lsmeans(lm_set_3.1_DR, trt.vs.ctrl~isolate,ref=37)$contrasts, alpha=0.1), main="Mean Dry Root Weight Differences of Inoculated Plants vs. Control", xlab = "Difference of Mean Estimates (mg)", ylab = "Contrasts")
```

> BCW201088 has a mean dry root weight estimate with confidence intervals that do not overlap with the control, but the lower CL of 201088 is very close to the upper CL of the control estimate.

### 3.1.2. Dry Shoot Weight

```{r, fig.width=10}
# linear model of dry shoot data
lm_set_3.1_DS <- lm(mg ~ 1 + isolate, set_3.1_DS)
op = par(mfrow=c(1,2))
plot(lm_set_3.1_DS, which = c(2,3))
par(op)
```

#### 3.1.2. - Anova

```{r}
# assess variance
anova(lm_set_3.1_DS)
```

#### 3.1.2. - LSMeans - Dunnett

```{r}
lsm_s3.1.dun.DS <- summary(lsmeans(lm_set_3.1_DS, trt.vs.ctrl ~isolate, ref=37)$contrasts, infer = c(T,T))
write.csv(lsm_s3.1.dun.DS, "./lsmeans_summary_tables/lsm_s3.1.dun.DS.csv")
```

#### 3.1.2. - CLD - (Pairwise by isolate)

```{r}
CLD(lsmeans(lm_set_3.1_DS, ~isolate), alpha=0.1)
plot(CLD(lsmeans(lm_set_3.1_DS, ~isolate), alpha=0.1), main="All pairwise comparisons for Mean Dry Shoot Weight", xlab = "Mean Weight (mg)", ylab = "Isolate ID")
```

#### 3.1.2. - CLD - Mean Estimate Differences to Control

Compact Letter Display of estimates for Dry Shoot Mean weight (mg) differences between plants that were inoculated with a mucilage isolates and the non-inoculated control.

```{r}
plot(CLD(lsmeans(lm_set_3.1_DS, trt.vs.ctrl~isolate,ref=37)$contrasts, alpha=0.1), main="Mean Dry Shoot Weight Differences of Inoculated Plants vs. Control", xlab = "Difference of Mean Estimates (mg)", ylab = "Contrasts")
```

> BCW201914 has a mean dry shoot weight that is nearly three times that of the control and the confidence intervals between the two do not overlap at an alpha level of 0.1.

### 3.1.3. Wet Root Weight

```{r, fig.width=10}
# linear model of wet root data
lm_set_3.1_WR <- lm(mg ~ 1 + isolate, set_3.1_WR)
op = par(mfrow=c(1,2))
plot(lm_set_3.1_WR, which = c(2,3))
par(op)
```

#### 3.1.3. - Anova

```{r}
# assess variance
anova(lm_set_3.1_WR)
```

#### 3.1.3. - LSMeans - Dunnett

```{r}
summary(lsmeans(lm_set_3.1_WR, trt.vs.ctrl ~isolate, ref=37)$contrasts, infer = c(T,T))
```

#### 3.1.3. - CLD - (Pairwise by isolate)

```{r}
CLD(lsmeans(lm_set_3.1_WR, ~isolate), alpha=0.1)
plot(CLD(lsmeans(lm_set_3.1_WR, ~isolate), alpha=0.1), main="All pairwise comparisons for Mean Wet Root Weight", xlab = "Mean Weight (mg)", ylab = "Isolate ID")
```

#### 3.1.3. - CLD - Mean Estimate Differences to Control

Compact Letter Display of estimates for Wet Root Mean weight (mg) differences between plants that were inoculated with a mucilage isolates and the non-inoculated control.

```{r}
plot(CLD(lsmeans(lm_set_3.1_WR, trt.vs.ctrl~isolate,ref=37)$contrasts, alpha=0.1), main="Mean Wet Root Weight Differences of Inoculated Plants vs. Control", xlab = "Difference of Mean Estimates (mg)", ylab = "Contrasts")
```

> The results for the wet root data agree with those of the dry root data.

### 3.1.4. Wet Shoot Weight

```{r, fig.width=10}
# linear model of wet shoot data
lm_set_3.1_WS <- lm(mg ~ 1 + isolate, set_3.1_WS)
op = par(mfrow=c(1,2))
plot(lm_set_3.1_WS, which = c(2,3))
par(op)
```

#### 3.1.4. - Anova

```{r}
# assess variance
anova(lm_set_3.1_WS)
```


#### 3.1.4. - LSMeans - Dunnett

```{r}
lsm_s3.1.dun.WS <- summary(lsmeans(lm_set_3.1_WS, trt.vs.ctrl ~isolate, ref=37)$contrasts, infer = c(T,T))
write.csv(lsm_s3.1.dun.WS, "./lsmeans_summary_tables/lsm_s3.1.dun.WS.csv")
```

#### 3.1.4. - CLD

```{r}
CLD(lsmeans(lm_set_3.1_WS, ~isolate), alpha=0.1)
plot(CLD(lsmeans(lm_set_3.1_WS, ~isolate), alpha=0.1), main="Set 3.1: All pairwise comparisons for Mean Wet Shoot Weight", xlab = "Mean Weight (mg)", ylab = "Isolate ID")
```

> Confidence intervals overlap between isolates with largest mean differences to the control.

## Batch 3.2
```{r}
# analyze set 3.2 data
str(set_3.2)

#subset by sample type
set_3.2_DR <- filter(set_3.2, sample == "DR")
set_3.2_DS <- filter(set_3.2, sample == "DS")
set_3.2_WR <- filter(set_3.2, sample == "WR")
set_3.2_WS <- filter(set_3.2, sample == "WS")
```

**Experimental Design Table**
  
  | Structure | Factor | Type | # levels |
  |-----------|--------|------|----------|
  | Treatment | Isolate | Qualitative | 26 |
  | Design    | MS Media Slants | Experimental Unit | 78 |
  | Response  | Plant Tissue Weight | Observational Unit | 312 |
  |Response | Dry Root Weight (DR) | Variable | 78 | 
  | Response | Dry Shoot Weight (DS) | Variable | 78 |
  | Response | Wet Root Weight (WR) | Variable | 78 | 
  | Response | Wet Shoot Weight (WS) | Variable | 78 |

### 3.2.1. Dry Root Weight

```{r, fig.width=10}
# linear model of dry root data
lm_set_3.2_DR <- lm(mg ~ 1 + isolate, set_3.2_DR)
op = par(mfrow=c(1,2))
plot(lm_set_3.2_DR, which = c(2,3))
par(op)
```

#### 3.2.1. - Anova

```{r}
# assess variance
anova(lm_set_3.2_DR)
```

#### 3.2.1. - LSMeans - Dunnett

```{r}
lsm_s3.2.dun.DR <- summary(lsmeans(lm_set_3.2_DR, trt.vs.ctrl ~isolate, ref=25)$contrasts, infer = c(T,T))
write.csv(lsm_s3.2.dun.DR, "./lsmeans_summary_tables/lsm_s3.2.dun.DR.csv")
```

#### 3.2.1. - CLD - (Pairwise by isolate)

```{r}
CLD(lsmeans(lm_set_3.2_DR, ~isolate), alpha=0.1)
plot(CLD(lsmeans(lm_set_3.2_DR, ~isolate), alpha=0.1), main="All pairwise comparisons for Mean Dry Root Weight", xlab = "Mean Weight (mg)", ylab = "Isolate ID")
```

#### 3.2.1. - CLD - Mean Estimate Differences to Control

Compact Letter Display of estimates for Dry Root Mean weight (mg) differences between plants that were inoculated with a mucilage isolates and the non-inoculated control.

```{r}
plot(CLD(lsmeans(lm_set_3.2_DR, trt.vs.ctrl~isolate,ref=25)$contrasts, alpha=0.1), main="Mean Dry Root Weight Differences of Inoculated Plants vs. Control", xlab = "Difference of Mean Estimates (mg)", ylab = "Contrasts")
```

> There are no significantly different means estimates

### 3.2.2. Dry Shoot Weight

```{r, fig.width=10}
# linear model of dry shoot data
lm_set_3.2_DS <- lm(mg ~ 1 + isolate, set_3.2_DS)
op = par(mfrow=c(1,2))
plot(lm_set_3.2_DS, which = c(2,3))
par(op)
```

#### 3.2.2. - Anova

```{r}
# assess variance
anova(lm_set_3.2_DS)
```

#### 3.2.2. - LSMeans - Dunnett

```{r}
lsm_s3.2.dun.DS <- summary(lsmeans(lm_set_3.2_DS, trt.vs.ctrl ~isolate, ref=25)$contrasts, infer = c(T,T))
write.csv(lsm_s3.2.dun.DS, "./lsmeans_summary_tables/lsm_s3.2.dun.DS.csv")
```

#### 3.2.2. - CLD - (Pairwise by isolate)

```{r}
CLD(lsmeans(lm_set_3.2_DS, ~isolate), alpha=0.1)
plot(CLD(lsmeans(lm_set_3.2_DS, ~isolate), alpha=0.1), main="All pairwise comparisons for Mean Dry Shoot Weight", xlab = "Mean Weight (mg)", ylab = "Isolate ID")
```

#### 3.2.2. - CLD - Mean Estimate Differences to Control

Compact Letter Display of estimates for Dry Shoot Mean weight (mg) differences between plants that were inoculated with a mucilage isolates and the non-inoculated control.

```{r}
plot(CLD(lsmeans(lm_set_3.2_DS, trt.vs.ctrl~isolate,ref=25)$contrasts, alpha=0.1), main="Mean Dry Shoot Weight Differences of Inoculated Plants vs. Control", xlab = "Difference of Mean Estimates (mg)", ylab = "Contrasts")
```

> There are no significantly different means estimates for Dry Shoot weight in Batch 3.2.

### 3.2.3. Wet Root Data

```{r, fig.width=10}
# linear model of wet root data
lm_set_3.2_WR <- lm(mg ~ 1 + isolate, set_3.2_WR)
op = par(mfrow=c(1,2))
plot(lm_set_3.2_WR, which = c(2,3))
par(op)
```

#### 3.2.3. - Anova

```{r}
# assess variance
anova(lm_set_3.2_WR)
```

#### 3.2.3. - LSMeans - Dunnett

```{r}
lsm_s3.2.dun.WR <- summary(lsmeans(lm_set_3.2_WR, trt.vs.ctrl ~isolate, ref=25)$contrasts, infer = c(T,T))
write.csv(lsm_s3.2.dun.WR, "./lsmeans_summary_tables/lsm_s3.2.dun.WR.csv")
```

#### 3.2.3. - CLD - (Pairwise by isolate)

```{r}
CLD(lsmeans(lm_set_3.2_WR, ~isolate), alpha=0.1)
plot(CLD(lsmeans(lm_set_3.2_WR, ~isolate), alpha=0.1), main="All pairwise comparisons for Mean Wet Root Weight", xlab = "Mean Weight (mg)", ylab = "Isolate ID")
```

#### 3.2.3. - CLD - Mean Estimate Differences to Control

Compact Letter Display of estimates for Wet Root Mean weight (mg) differences between plants that were inoculated with a mucilage isolates and the non-inoculated control.

```{r}
plot(CLD(lsmeans(lm_set_3.2_WR, trt.vs.ctrl~isolate,ref=25)$contrasts, alpha=0.1), main="Mean Wet Root Weight Differences of Inoculated Plants vs. Control", xlab = "Difference of Mean Estimates (mg)", ylab = "Contrasts")
```

> There are no significantly different means estimates for wet root weight in batch 3.2.

### 3.2.4. Wet Shoot Data

```{r, fig.width=10}
# linear model of wet shoot data
lm_set_3.2_WS <- lm(mg ~ 1 + isolate, set_3.2_WS)
op = par(mfrow=c(1,2))
plot(lm_set_3.2_WS, which = c(2,3))
par(op)
```

#### 3.2.4. - Anova

```{r}
# assess variance
anova(lm_set_3.2_WS)
```

#### 3.2.4. - LSMeans - Dunnett

```{r}
lsm_s3.2.dun.WS <- summary(lsmeans(lm_set_3.2_WS, trt.vs.ctrl ~isolate, ref=25)$contrasts, infer = c(T,T))
write.csv(lsm_s3.2.dun.WS, "./lsmeans_summary_tables/lsm_s3.2.dun.WS.csv")
```

#### 3.2.4. - CLD - (Pairwise by isolate)

```{r}
CLD(lsmeans(lm_set_3.2_WS, ~isolate), alpha=0.1)
plot(CLD(lsmeans(lm_set_3.2_WS, ~isolate), alpha=0.1), main="All pairwise comparisons for Mean Wet Shoot Weight", xlab = "Mean Weight (mg)", ylab = "Isolate ID")
```

#### 3.2.4. - CLD - Mean Estimate Differences to Control

Compact Letter Display of estimates for Wet Shoot Mean weight (mg) differences between plants that were inoculated with a mucilage isolates and the non-inoculated control.

```{r}
plot(CLD(lsmeans(lm_set_3.2_WS, trt.vs.ctrl~isolate,ref=25)$contrasts, alpha=0.1), main="Mean Wet Shoot Weight Differences of Inoculated Plants vs. Control", xlab = "Difference of Mean Estimates (mg)", ylab = "Contrasts")
```

> There are no significantly different means estimates. Control is the highest mean, all inoculated platlets exhibited lower mean wet shoot weight.

## Batch 3.3

```{r}
# analyze set 3.3 data
str(set_3.3)

#subset by sample type

set_3.3_DR <- filter(set_3.3, sample == "DR")
set_3.3_DS <- filter(set_3.3, sample == "DS")
set_3.3_WR <- filter(set_3.3, sample == "WR")
set_3.3_WS <- filter(set_3.3, sample == "WS")

```

**Experimental Design Table**
  
  | Structure | Factor | Type | # levels |
  |-----------|--------|------|----------|
  | Treatment | Isolate | Qualitative | 38 |
  | Design    | MS Media Slants | Experimental Unit | 114 |
  | Response  | Plant Tissue Weight | Observational Unit | 456 |
  |Response | Dry Root Weight (DR) | Variable | 114 | 
  | Response | Dry Shoot Weight (DS) | Variable | 114 |
  | Response | Wet Root Weight (WR) | Variable | 114 | 
  | Response | Wet Shoot Weight (WS) | Variable | 114 |

### 3.3.1. Dry Root Weight

```{r, fig.width=10}
# linear model of dry root data
lm_set_3.3_DR <- lm(mg ~ 1 + isolate, set_3.3_DR)
op = par(mfrow=c(1,2))
plot(lm_set_3.3_DR, which = c(2,3))
par(op)
```

#### 3.3.1. - Anova

```{r}
# assess variance
anova(lm_set_3.3_DR)
```

#### 3.3.1. - LSMeans - Dunnett

```{r}
lsm_s3.3.dun.DR <- summary(lsmeans(lm_set_3.3_DR, trt.vs.ctrl ~isolate, ref=37)$contrasts, infer = c(T,T))
write.csv(lsm_s3.3.dun.DR, "./lsmeans_summary_tables/lsm_s3.3.dun.DR.csv")
```

#### 3.3.1. - CLD - (Pairwise by isolate)

```{r}
CLD(lsmeans(lm_set_3.3_DR, ~isolate), alpha=0.1)
plot(CLD(lsmeans(lm_set_3.3_DR, ~isolate), alpha=0.1), main="All pairwise comparisons for Mean Dry Root Weight", xlab = "Mean Weight (mg)", ylab = "Isolate ID")
```

#### 3.3.1. - CLD - Mean Estimate Differences to Control

Compact Letter Display of estimates for Dry Root Mean weight (mg) differences between plants that were inoculated with a mucilage isolates and the non-inoculated control.

```{r}
plot(CLD(lsmeans(lm_set_3.3_DR, trt.vs.ctrl~isolate,ref=37)$contrasts, alpha=0.1), main="Mean Dry Root Weight Differences of Inoculated Plants vs. Control", xlab = "Difference of Mean Estimates (mg)", ylab = "Contrasts")
```

> Here, the control mean is much higher than most of the inoculated samples.

### 3.3.2. Dry Shoot Weight

```{r, fig.width=10}
# linear model of dry shoot data
lm_set_3.3_DS <- lm(mg ~ 1 + isolate, set_3.3_DS)
op = par(mfrow=c(1,2))
plot(lm_set_3.3_DS, which = c(2,3))
par(op)
```

#### 3.3.2. - Anova

```{r}
# assess variance
anova(lm_set_3.3_DS)
```

#### 3.3.2. - LSMeans - Dunnett

```{r}
lsm_s3.3.dun.DS <- summary(lsmeans(lm_set_3.3_DS, trt.vs.ctrl ~isolate, ref=37)$contrasts, infer = c(T,T))
write.csv(lsm_s3.3.dun.DS, "./lsmeans_summary_tables/lsm_s3.3.dun.DS.csv")
```

#### 3.3.2. - CLD - (Pairwise by isolate)

```{r}
CLD(lsmeans(lm_set_3.3_DS, ~isolate), alpha=0.1)
plot(CLD(lsmeans(lm_set_3.3_DS, ~isolate), alpha=0.1), main="All pairwise comparisons for Mean Dry Shoot Weight", xlab = "Mean Weight (mg)", ylab = "Isolate ID")
```

#### 3.3.2. - CLD - Mean Estimate Differences to Control

Compact Letter Display of estimates for Dry Shoot Mean weight (mg) differences between plants that were inoculated with a mucilage isolates and the non-inoculated control.

```{r}
plot(CLD(lsmeans(lm_set_3.3_DS, trt.vs.ctrl~isolate,ref=37)$contrasts, alpha=0.1), main="Mean Dry Shoot Weight Differences of Inoculated Plants vs. Control", xlab = "Difference of Mean Estimates (mg)", ylab = "Contrasts")
```

> Again, we have negative differences in mean weight relative to the control.

### 3.3.3. Wet Root Weight

```{r, fig.width=10}
# linear model of wet root data
lm_set_3.3_WR <- lm(mg ~ 1 + isolate, set_3.3_WR)
op = par(mfrow=c(1,2))
plot(lm_set_3.3_WR, which = c(2,3))
par(op)
```

#### 3.3.3. - Anova

```{r}
# assess variance
anova(lm_set_3.3_WR)
```

#### 3.3.3. - LSMeans - Dunnett

```{r}
lsm_s3.3.dun.WR <- summary(lsmeans(lm_set_3.3_WR, trt.vs.ctrl ~isolate, ref=37)$contrasts, infer = c(T,T))
write.csv(lsm_s3.3.dun.WR, "./lsmeans_summary_tables/lsm_s3.3.dun.WR.csv")
```

#### 3.3.3. - CLD - (Pairwise by isolate)

```{r}
CLD(lsmeans(lm_set_3.3_WR, ~isolate), alpha=0.1)
plot(CLD(lsmeans(lm_set_3.3_WR, ~isolate), alpha=0.1), main="All pairwise comparisons for Mean Wet Root Weight", xlab = "Mean Weight (mg)", ylab = "Isolate ID")
```

#### 3.3.3. - CLD - Mean Estimate Differences to Control

Compact Letter Display of estimates for Wet Root Mean weight (mg) differences between plants that were inoculated with a mucilage isolates and the non-inoculated control.

```{r}
plot(CLD(lsmeans(lm_set_3.3_WR, trt.vs.ctrl~isolate,ref=37)$contrasts, alpha=0.1), main="Mean Wet Root Weight Differences of Inoculated Plants vs. Control", xlab = "Difference of Mean Estimates (mg)", ylab = "Contrasts")
```

> Effect of data distribution, or control is bad, or the isolates hinder growth?

### 3.3.4. Wet Shoot Weight

```{r, fig.width=10}
# linear model of wet shoot data
lm_set_3.3_WS <- lm(mg ~ 1 + isolate, set_3.3_WS)
op = par(mfrow=c(1,2))
plot(lm_set_3.3_WS, which = c(2,3))
par(op)
```

#### 3.3.4. - Anova

```{r}
# assess variance
anova(lm_set_3.3_WS)
```

#### 3.3.4. - LSMeans - Dunnett

```{r}
lsm_s3.3.dun.WS <- summary(lsmeans(lm_set_3.3_WS, trt.vs.ctrl ~isolate, ref=37)$contrasts, infer = c(T,T))
write.csv(lsm_s3.3.dun.WS, "./lsmeans_summary_tables/lsm_s3.3.dun.WS.csv")
```

#### 3.3.4. - CLD - (Pairwise by isolate)

```{r}
CLD(lsmeans(lm_set_3.3_WS, ~isolate), alpha=0.1)
plot(CLD(lsmeans(lm_set_3.3_WS, ~isolate), alpha=0.1), main="All pairwise comparisons for Mean Wet Shoot Weight", xlab = "Mean Weight (mg)", ylab = "Isolate ID")
```

#### 3.3.4. - CLD - Mean Estimate Differences to Control

Compact Letter Display of estimates for Wet Shoot Mean weight (mg) differences between plants that were inoculated with a mucilage isolates and the non-inoculated control.

```{r}
plot(CLD(lsmeans(lm_set_3.3_WS, trt.vs.ctrl~isolate,ref=37)$contrasts, alpha=0.1), main="Mean Wet Shoot Weight Differences of Inoculated Plants vs. Control", xlab = "Difference of Mean Estimates (mg)", ylab = "Contrasts")
```

> The control mean weight is much higher than inoculated plant mean weights for wet shoot.

## Batch 3.4

```{r}
# analyze set 3.4 data
str(set_3.4)

#subset by sample type

set_3.4_DR <- filter(set_3.4, sample == "DR")
set_3.4_DS <- filter(set_3.4, sample == "DS")
set_3.4_WR <- filter(set_3.4, sample == "WR")
set_3.4_WS <- filter(set_3.4, sample == "WS")

```

**Experimental Design Table**
  
  | Structure | Factor | Type | # levels |
  |-----------|--------|------|----------|
  | Treatment | Isolate | Qualitative | 38 |
  | Design    | MS Media Slants | Experimental Unit | 114 |
  | Response  | Plant Tissue Weight | Observational Unit | 456 |
  |Response | Dry Root Weight (DR) | Variable | 114 | 
  | Response | Dry Shoot Weight (DS) | Variable | 114 |
  | Response | Wet Root Weight (WR) | Variable | 114 | 
  | Response | Wet Shoot Weight (WS) | Variable | 114 |

### 3.4.1. Dry Root Weight

```{r, fig.width=10}
# linear model of dry root data
lm_set_3.4_DR <- lm(mg ~ 1 + isolate, set_3.4_DR)
op = par(mfrow=c(1,2))
plot(lm_set_3.4_DR, which = c(2,3))
par(op)
```

#### 3.4.1. - Anova

```{r}
# assess variance
anova(lm_set_3.4_DR)
```

#### 3.4.1. - LSMeans - Dunnett

```{r}
lsm_s3.4.dun.DR <- summary(lsmeans(lm_set_3.4_DR, trt.vs.ctrl ~isolate, ref=36)$contrasts, infer = c(T,T))
write.csv(lsm_s3.4.dun.DR, "./lsmeans_summary_tables/lsm_s3.4.dun.DR.csv")
```

#### 3.4.1. - CLD - (Pairwise by isolate)

```{r}
CLD(lsmeans(lm_set_3.4_DR, ~isolate), alpha=0.1)
plot(CLD(lsmeans(lm_set_3.4_DR, ~isolate), alpha=0.1), main="All pairwise comparisons for Mean Dry Root Weight", xlab = "Mean Weight (mg)", ylab = "Isolate ID")
```

#### 3.4.1. - CLD - Mean Estimate Differences to Control

Compact Letter Display of estimates for Dry Root Mean weight (mg) differences between plants that were inoculated with a mucilage isolates and the non-inoculated control.

```{r}
plot(CLD(lsmeans(lm_set_3.4_DR, trt.vs.ctrl~isolate,ref=36)$contrasts, alpha=0.1), main="Mean Dry Root Weight Differences of Inoculated Plants vs. Control", xlab = "Difference of Mean Estimates (mg)", ylab = "Contrasts")
```

> There are not significant positive differences between mean estimates for inoculated plants and the control plants with respect to dry root weight measurements in batch 3.4.

### 3.4.2. Dry Shoot Weight

```{r, fig.width=10}
# linear model of dry shoot data
lm_set_3.4_DS <- lm(mg ~ 1 + isolate, set_3.4_DS)
op = par(mfrow=c(1,2))
plot(lm_set_3.4_DS, which = c(2,3))
par(op)
```

#### 3.4.2. - Anova

```{r}
# assess variance
anova(lm_set_3.4_DS)
```

#### 3.4.2. - LSMeans - Dunnett

```{r}
lsm_s3.4.dun.DS <- summary(lsmeans(lm_set_3.4_DS, trt.vs.ctrl ~isolate, ref=36)$contrasts, infer = c(T,T))
write.csv(lsm_s3.4.dun.DS, "./lsmeans_summary_tables/lsm_s3.4.dun.DS.csv")
```

#### 3.4.2. - CLD - (Pairwise by isolate)

```{r}
CLD(lsmeans(lm_set_3.4_DS, ~isolate), alpha=0.1)
plot(CLD(lsmeans(lm_set_3.4_DS, ~isolate), alpha=0.1), main="All pairwise comparisons for Mean Dry Shoot Weight", xlab = "Mean Weight (mg)", ylab = "Isolate ID")
```

#### 3.4.2. - CLD - Mean Estimate Differences to Control

Compact Letter Display of estimates for Dry Shoot Mean weight (mg) differences between plants that were inoculated with a mucilage isolates and the non-inoculated control.

```{r}
plot(CLD(lsmeans(lm_set_3.4_DS, trt.vs.ctrl~isolate,ref=36)$contrasts, alpha=0.1), main="Mean Dry Shoot Weight Differences of Inoculated Plants vs. Control", xlab = "Difference of Mean Estimates (mg)", ylab = "Contrasts")
```

> All isolates have mean estimates for dry shoot mass with confidence intervals that overlap with those of the control group.

### 3.4.3. Wet Root Weight

```{r, fig.width=10}
# linear model of wet root data
lm_set_3.4_WR <- lm(mg ~ 1 + isolate, set_3.4_WR)
op = par(mfrow=c(1,2))
plot(lm_set_3.4_WR, which = c(2,3))
par(op)
```

#### 3.4.3. - Anova

```{r}
# assess variance
anova(lm_set_3.4_WR)
```

#### 3.4.3. - LSMeans - Dunnett

```{r}
lsm_s3.4.dun.WR <- summary(lsmeans(lm_set_3.4_WR, trt.vs.ctrl ~isolate, ref=36)$contrasts, infer = c(T,T))
write.csv(lsm_s3.4.dun.WR, "./lsmeans_summary_tables/lsm_s3.4.dun.WR.csv")
```

#### 3.4.3. - CLD - (Pairwise by isolate)

```{r}
CLD(lsmeans(lm_set_3.4_WR, ~isolate), alpha=0.1)
plot(CLD(lsmeans(lm_set_3.4_WR, ~isolate), alpha=0.1), main="All pairwise comparisons for Mean Wet Root Weight", xlab = "Mean Weight (mg)", ylab = "Isolate ID")
```

#### 3.4.3. - CLD - Mean Estimate Differences to Control

Compact Letter Display of estimates for Wet Root Mean weight (mg) differences between plants that were inoculated with a mucilage isolates and the non-inoculated control.

```{r}
plot(CLD(lsmeans(lm_set_3.4_WR, trt.vs.ctrl~isolate,ref=36)$contrasts, alpha=0.1), main="Mean Wet Root Weight Differences of Inoculated Plants vs. Control", xlab = "Difference of Mean Estimates (mg)", ylab = "Contrasts")
```

> No significant effect of inoculation treatment on mean wet root weight for batch 3.4 was observed.

### 3.4.4. Wet Shoot Weight

```{r, fig.width=10}
# linear model of wet shoot data
lm_set_3.4_WS <- lm(mg ~ 1 + isolate, set_3.4_WS)
op = par(mfrow=c(1,2))
plot(lm_set_3.4_WS, which = c(2,3))
par(op)
```

#### 3.4.4. - Anova

```{r}
# assess variance
anova(lm_set_3.4_WS)
```

#### 3.4.4. - LSMeans - Dunnett

```{r}
lsm_s3.4.dun.WS <- summary(lsmeans(lm_set_3.4_WS, trt.vs.ctrl ~isolate, ref=36)$contrasts, infer = c(T,T))
write.csv(lsm_s3.4.dun.WS, "./lsmeans_summary_tables/lsm_s3.4.dun.WS.csv")
```

#### 3.4.4. - CLD - (Pairwise by isolate)

```{r}
CLD(lsmeans(lm_set_3.4_WS, ~isolate), alpha=0.1)
plot(CLD(lsmeans(lm_set_3.4_WS, ~isolate), alpha=0.1), main="All pairwise comparisons for Mean Wet Shoot Weight", xlab = "Mean Weight (mg)", ylab = "Isolate ID")
```

#### 3.4.4. - CLD - Mean Estimate Differences to Control

Compact Letter Display of estimates for Wet Shoot Mean weight (mg) differences between plants that were inoculated with a mucilage isolates and the non-inoculated control.

```{r}
plot(CLD(lsmeans(lm_set_3.4_WS, trt.vs.ctrl~isolate,ref=36)$contrasts, alpha=0.1), main="Mean Wet Shoot Weight Differences of Inoculated Plants vs. Control", xlab = "Difference of Mean Estimates (mg)", ylab = "Contrasts")
```

> No significant effect of inoculation treatment on mean wet shoot weight for batch 3.4 was observed. Confidence intervals for mean estimates of isolates that produced positive weight differences relative to the control are highly overlapping.

## Batch 3.5

```{r}
# analyze set 3.5 data
str(set_3.5)

#subset by sample type

set_3.5_DR <- filter(set_3.5, sample == "DR")
set_3.5_DS <- filter(set_3.5, sample == "DS")
set_3.5_WR <- filter(set_3.5, sample == "WR")
set_3.5_WS <- filter(set_3.5, sample == "WS")

```

**Experimental Design Table**
  
  | Structure | Factor | Type | # levels |
  |-----------|--------|------|----------|
  | Treatment | Isolate | Qualitative | 26 |
  | Design    | MS Media Slants | Experimental Unit | 78 |
  | Response  | Plant Tissue Weight | Observational Unit | 312 |
  |Response | Dry Root Weight (DR) | Variable | 78 | 
  | Response | Dry Shoot Weight (DS) | Variable | 78 |
  | Response | Wet Root Weight (WR) | Variable | 78 | 
  | Response | Wet Shoot Weight (WS) | Variable | 78 |

### 3.5.1. Dry Root Weight

```{r, fig.width=10}
# linear model of dry root data
lm_set_3.5_DR <- lm(mg ~ 1 + isolate, set_3.5_DR)
op = par(mfrow=c(1,2))
plot(lm_set_3.5_DR, which = c(2,3))
par(op)
```

#### 3.5.1. - Anova

```{r}
# assess variance
anova(lm_set_3.5_DR)
```

#### 3.5.1. - LSMeans - Dunnett

```{r}
lsm_s3.5.dun.DR <- summary(lsmeans(lm_set_3.5_DR, trt.vs.ctrl ~isolate, ref=22)$contrasts, infer = c(T,T))
write.csv(lsm_s3.5.dun.DR, "./lsmeans_summary_tables/lsm_s3.5.dun.DR.csv")
```

#### 3.5.1. - CLD - (Pairwise by isolate)

```{r}
CLD(lsmeans(lm_set_3.5_DR, ~isolate), alpha=0.1)
plot(CLD(lsmeans(lm_set_3.5_DR, ~isolate), alpha=0.1), main="All pairwise comparisons for Mean Dry Root Weight", xlab = "Mean Weight (mg)", ylab = "Isolate ID")
```

#### 3.5.1. - CLD - Mean Estimate Differences to Control

Compact Letter Display of estimates for Dry Root Mean weight (mg) differences between plants that were inoculated with a mucilage isolates and the non-inoculated control.

```{r}
plot(CLD(lsmeans(lm_set_3.5_DR, trt.vs.ctrl~isolate,ref=22)$contrasts, alpha=0.1), main="Mean Dry Root Weight Differences of Inoculated Plants vs. Control", xlab = "Difference of Mean Estimates (mg)", ylab = "Contrasts")
```

> No significant effect.

### 3.5.2. Dry Shoot Weight

```{r, fig.width=10}
# linear model of dry shoot data
lm_set_3.5_DS <- lm(mg ~ 1 + isolate, set_3.5_DS)
op = par(mfrow=c(1,2))
plot(lm_set_3.5_DS, which = c(2,3))
par(op)
```

#### 3.5.2. - Anova

```{r}
# assess variance
anova(lm_set_3.5_DS)
```

#### 3.5.2. - LSMeans - Dunnett

```{r}
lsm_s3.5.dun.DS <- summary(lsmeans(lm_set_3.5_DS, trt.vs.ctrl ~isolate, ref=22)$contrasts, infer = c(T,T))
write.csv(lsm_s3.5.dun.DS, "./lsmeans_summary_tables/lsm_s3.5.dun.DS.csv")
```

#### 3.5.2. - CLD - (Pairwise by isolate)

```{r}
CLD(lsmeans(lm_set_3.5_DS, ~isolate), alpha=0.1)
plot(CLD(lsmeans(lm_set_3.5_DS, ~isolate), alpha=0.1), main="All pairwise comparisons for Mean Dry Shoot Weight", xlab = "Mean Weight (mg)", ylab = "Isolate ID")
```

#### 3.5.2. - CLD - Mean Estimate Differences to Control

Compact Letter Display of estimates for Dry Shoot Mean weight (mg) differences between plants that were inoculated with a mucilage isolates and the non-inoculated control.

```{r}
plot(CLD(lsmeans(lm_set_3.5_DS, trt.vs.ctrl~isolate,ref=22)$contrasts, alpha=0.1), main="Mean Dry Shoot Weight Differences of Inoculated Plants vs. Control", xlab = "Difference of Mean Estimates (mg)", ylab = "Contrasts")
```

> No significant positive effects were observed for mean dry shoot weight in batch 3.5.

### 3.5.3. Wet Root Weight

```{r, fig.width=10}
# linear model of wet root data
lm_set_3.5_WR <- lm(mg ~ 1 + isolate, set_3.5_WR)
op = par(mfrow=c(1,2))
plot(lm_set_3.5_WR, which = c(2,3))
par(op)
```

#### 3.5.3. - Anova

```{r}
# assess variance
anova(lm_set_3.5_WR)
```

#### 3.5.3. - LSMeans - Dunnett

```{r}
lsm_s3.5.dun.WR <- summary(lsmeans(lm_set_3.5_WR, trt.vs.ctrl ~isolate, ref=22)$contrasts, infer = c(T,T))
write.csv(lsm_s3.5.dun.WR, "./lsmeans_summary_tables/lsm_s3.5.dun.WR.csv")
```

#### 3.5.3. - CLD - (Pairwise by isolate)

```{r}
CLD(lsmeans(lm_set_3.5_WR, ~isolate), alpha=0.1)
plot(CLD(lsmeans(lm_set_3.5_WR, ~isolate), alpha=0.1), main="All pairwise comparisons for Mean Wet Root Weight", xlab = "Mean Weight (mg)", ylab = "Isolate ID")
```

#### 3.5.3. - CLD - Mean Estimate Differences to Control

Compact Letter Display of estimates for Wet Root Mean weight (mg) differences between plants that were inoculated with a mucilage isolates and the non-inoculated control.

```{r}
plot(CLD(lsmeans(lm_set_3.5_WR, trt.vs.ctrl~isolate,ref=22)$contrasts, alpha=0.1), main="Mean Wet Root Weight Differences of Inoculated Plants vs. Control", xlab = "Difference of Mean Estimates (mg)", ylab = "Contrasts")
```

> No significant positive effects observed for wet root weight of batch 3.5.

### 3.5.4. Wet Shoot Weight

```{r, fig.width=10}
# linear model of wet shoot data
lm_set_3.5_WS <- lm(mg ~ 1 + isolate, set_3.5_WS)
op = par(mfrow=c(1,2))
plot(lm_set_3.5_WS, which = c(2,3))
par(op)
```

#### 3.5.4. - Anova

```{r}
# assess variance
anova(lm_set_3.5_WS)
```

#### 3.5.4. - LSMeans - Dunnett

```{r}
lsm_s3.5.dun.WS <- summary(lsmeans(lm_set_3.5_WS, trt.vs.ctrl ~isolate, ref=22)$contrasts, infer = c(T,T))
write.csv(lsm_s3.5.dun.WS, "./lsmeans_summary_tables/lsm_s3.5.dun.WS.csv")
```

#### 3.5.4. - CLD - (Pairwise by isolate)

```{r}
CLD(lsmeans(lm_set_3.5_WS, ~isolate), alpha=0.1)
plot(CLD(lsmeans(lm_set_3.5_WS, ~isolate), alpha=0.1), main="All pairwise comparisons for Mean Wet Shoot Weight", xlab = "Mean Weight (mg)", ylab = "Isolate ID")
```

#### 3.5.4. - CLD - Mean Estimate Differences to Control

Compact Letter Display of estimates for Wet Shoot Mean weight (mg) differences between plants that were inoculated with a mucilage isolates and the non-inoculated control.

```{r}
plot(CLD(lsmeans(lm_set_3.5_WS, trt.vs.ctrl~isolate,ref=22)$contrasts, alpha=0.1), main="Mean Wet Shoot Weight Differences of Inoculated Plants vs. Control", xlab = "Difference of Mean Estimates (mg)", ylab = "Contrasts")
```

> No significant positive effect was observed for Wet Shoot weight in batch 3.5 inoculations.

## Batch 4.1

```{r}
# analyze set 4.1 data
str(set_4.1)

#subset by sample type

set_4.1_DR <- filter(set_4.1, sample == "DR")
set_4.1_DS <- filter(set_4.1, sample == "DS")
set_4.1_WR <- filter(set_4.1, sample == "WR")
set_4.1_WS <- filter(set_4.1, sample == "WS")

```

**Experimental Design Table**
  
  | Structure | Factor | Type | # levels |
  |-----------|--------|------|----------|
  | Treatment | Isolate | Qualitative | 39 |
  | Design    | MS Media Slants | Experimental Unit | 117 |
  | Response  | Plant Tissue Weight | Observational Unit | 468 |
  |Response | Dry Root Weight (DR) | Variable | 117 | 
  | Response | Dry Shoot Weight (DS) | Variable | 117 |
  | Response | Wet Root Weight (WR) | Variable | 117 | 
  | Response | Wet Shoot Weight (WS) | Variable | 117 |
  
### 4.1.1. Dry Root Weight

```{r, fig.width=10}
# linear model of dry root data
lm_set_4.1_DR <- lm(mg ~ 1 + isolate, set_4.1_DR)
op = par(mfrow=c(1,2))
plot(lm_set_4.1_DR, which = c(2,3))
par(op)
```

#### 4.1.1. - Anova

```{r}
# assess variance
anova(lm_set_4.1_DR)
```

#### 4.1.1. - LSMeans - Dunnett

```{r}
lsm_s4.1.dun.DR <- summary(lsmeans(lm_set_4.1_DR, trt.vs.ctrl ~isolate, ref=37)$contrasts, infer = c(T,T))
write.csv(lsm_s4.1.dun.DR, "./lsmeans_summary_tables/lsm_s4.1.dun.DR.csv")
```

#### 4.1.1. - CLD - (Pairwise by isolate)

```{r}
CLD(lsmeans(lm_set_4.1_DR, ~isolate), alpha=0.1)
plot(CLD(lsmeans(lm_set_4.1_DR, ~isolate), alpha=0.1), main="All pairwise comparisons for Mean Dry Root Weight", xlab = "Mean Weight (mg)", ylab = "Isolate ID")
```

#### 4.1.1. - CLD - Mean Estimate Differences to Control

Compact Letter Display of estimates for Dry Root Mean weight (mg) differences between plants that were inoculated with a mucilage isolates and the non-inoculated control.

```{r}
plot(CLD(lsmeans(lm_set_4.1_DR, trt.vs.ctrl~isolate,ref=37)$contrasts, alpha=0.1), main="Mean Dry Root Weight Differences of Inoculated Plants vs. Control", xlab = "Difference of Mean Estimates (mg)", ylab = "Contrasts")
```

> No significant positive effect observed for inoculum treatment based on dry root measurements of batch 4.1.

### 4.1.2. Dry Shoot Weight

```{r, fig.width=10}
# linear model of dry shoot data
lm_set_4.1_DS <- lm(mg ~ 1 + isolate, set_4.1_DS)
op = par(mfrow=c(1,2))
plot(lm_set_4.1_DS, which = c(2,3))
par(op)
```

#### 4.1.2. - Anova

```{r}
# assess variance
anova(lm_set_4.1_DS)
```

#### 4.1.2. - LSMeans - Dunnett

```{r}
lsm_s4.1.dun.DS <- summary(lsmeans(lm_set_4.1_DS, trt.vs.ctrl ~isolate, ref=37)$contrasts, infer = c(T,T))
write.csv(lsm_s4.1.dun.DS, "./lsmeans_summary_tables/lsm_s4.1.dun.DS.csv")
```

#### 4.1.2. - CLD - (Pairwise by isolate)

```{r}
CLD(lsmeans(lm_set_4.1_DS, ~isolate), alpha=0.1)
plot(CLD(lsmeans(lm_set_4.1_DS, ~isolate), alpha=0.1), main="All pairwise comparisons for Mean Dry Shoot Weight", xlab = "Mean Weight (mg)", ylab = "Isolate ID")
```

#### 4.1.2. - CLD - Mean Estimate Differences to Control

Compact Letter Display of estimates for Dry Shoot Mean weight (mg) differences between plants that were inoculated with a mucilage isolates and the non-inoculated control.

```{r}
plot(CLD(lsmeans(lm_set_4.1_DS, trt.vs.ctrl~isolate,ref=37)$contrasts, alpha=0.1), main="Mean Dry Shoot Weight Differences of Inoculated Plants vs. Control", xlab = "Difference of Mean Estimates (mg)", ylab = "Contrasts")
```

> There are no significant positive effects observed based on measurements of dry shoot weight for plants inoculated with isolates in batch 4.1.

### 4.1.3. Wet Root Weight

```{r, fig.width=10}
# linear model of wet root data
lm_set_4.1_WR <- lm(mg ~ 1 + isolate, set_4.1_WR)
op = par(mfrow=c(1,2))
plot(lm_set_4.1_WR, which = c(2,3))
par(op)
```

#### 4.1.3. - Anova

```{r}
# assess variance
anova(lm_set_4.1_WR)
```

#### 4.1.3. - LSMeans - Dunnett

```{r}
lsm_s4.1.dun.WR <- summary(lsmeans(lm_set_4.1_WR, trt.vs.ctrl ~isolate, ref=37)$contrasts, infer = c(T,T))
write.csv(lsm_s4.1.dun.WR, "./lsmeans_summary_tables/lsm_s4.1.dun.WR.csv")
```

#### 4.1.3. - CLD - (Pairwise by isolate)

```{r}
CLD(lsmeans(lm_set_4.1_WR, ~isolate), alpha=0.1)
plot(CLD(lsmeans(lm_set_4.1_WR, ~isolate), alpha=0.1), main="All pairwise comparisons for Mean Wet Root Weight", xlab = "Mean Weight (mg)", ylab = "Isolate ID")
```

#### 4.1.3. - CLD - Mean Estimate Differences to Control

Compact Letter Display of estimates for Wet Root Mean weight (mg) differences between plants that were inoculated with a mucilage isolates and the non-inoculated control.

```{r}
plot(CLD(lsmeans(lm_set_4.1_WR, trt.vs.ctrl~isolate,ref=37)$contrasts, alpha=0.1), main="Mean Wet Root Weight Differences of Inoculated Plants vs. Control", xlab = "Difference of Mean Estimates (mg)", ylab = "Contrasts")
```

> Control is the highest mean estimate. All significant effects are negative on mean wet root weight.

### 4.1.4. Wet Shoot Weight

```{r, fig.width=10}
# linear model of wet shoot data
lm_set_4.1_WS <- lm(mg ~ 1 + isolate, set_4.1_WS)
op = par(mfrow=c(1,2))
plot(lm_set_4.1_WS, which = c(2,3))
par(op)
```

#### 4.1.4. - Anova

```{r}
# assess variance
anova(lm_set_4.1_WS)
```

#### 4.1.4. - LSMeans - Dunnett

```{r}
lsm_s4.1.dun.WS <- summary(lsmeans(lm_set_4.1_WS, trt.vs.ctrl ~isolate, ref=37)$contrasts, infer = c(T,T))
write.csv(lsm_s4.1.dun.WS, "./lsmeans_summary_tables/lsm_s4.1.dun.WS.csv")
```

#### 4.1.4. - CLD - (Pairwise by isolate)

```{r}
CLD(lsmeans(lm_set_4.1_WS, ~isolate), alpha=0.1)
plot(CLD(lsmeans(lm_set_4.1_WS, ~isolate), alpha=0.1), main="All pairwise comparisons for Mean Wet Shoot Weight", xlab = "Mean Weight (mg)", ylab = "Isolate ID")
```

#### 4.1.4. - CLD - Mean Estimate Differences to Control

Compact Letter Display of estimates for Wet Root Mean weight (mg) differences between plants that were inoculated with a mucilage isolates and the non-inoculated control.

```{r}
plot(CLD(lsmeans(lm_set_4.1_WS, trt.vs.ctrl~isolate,ref=37)$contrasts, alpha=0.1), main="Mean Wet Root Weight Differences of Inoculated Plants vs. Control", xlab = "Difference of Mean Estimates (mg)", ylab = "Contrasts")
```

> Highest mean producing isolate (BCW200984) has lower Confidence limit that overlaps with upper of control.

## Batch 4.2

```{r}
# analyze set 4.2 data
str(set_4.2)

#subset by sample type

set_4.2_DR <- filter(set_4.2, sample == "DR")
set_4.2_DS <- filter(set_4.2, sample == "DS")
set_4.2_WR <- filter(set_4.2, sample == "WR")
set_4.2_WS <- filter(set_4.2, sample == "WS")

```

**Experimental Design Table**
  
  | Structure | Factor | Type | # levels |
  |-----------|--------|------|----------|
  | Treatment | Isolate | Qualitative | 33 |
  | Design    | MS Media Slants | Experimental Unit | 99 |
  | Response  | Plant Tissue Weight | Observational Unit | 396 |
  |Response | Dry Root Weight (DR) | Variable | 99 | 
  | Response | Dry Shoot Weight (DS) | Variable | 99 |
  | Response | Wet Root Weight (WR) | Variable | 99 | 
  | Response | Wet Shoot Weight (WS) | Variable | 99 |

### 4.2.1. Dry Root Weight

```{r, fig.width=10}
# linear model of dry root data
lm_set_4.2_DR <- lm(mg ~ 1 + isolate, set_4.2_DR)
op = par(mfrow=c(1,2))
plot(lm_set_4.2_DR, which = c(2,3))
par(op)
```

#### 4.2.1. - Anova

```{r}
# assess variance
anova(lm_set_4.2_DR)
```

#### 4.2.1. - LSMeans - Dunnett

```{r}
lsm_s4.2.dun.DR <- summary(lsmeans(lm_set_4.2_DR, trt.vs.ctrl ~isolate, ref=24)$contrasts, infer = c(T,T))
write.csv(lsm_s4.2.dun.DR, "./lsmeans_summary_tables/lsm_s4.2.dun.DR.csv")
```

#### 4.2.1. - CLD - (Pairwise by isolate)

```{r}
CLD(lsmeans(lm_set_4.2_DR, ~isolate), alpha=0.1)
plot(CLD(lsmeans(lm_set_4.2_DR, ~isolate), alpha=0.1), main="All pairwise comparisons for Mean Dry Root Weight", xlab = "Mean Weight (mg)", ylab = "Isolate ID")
```

#### 4.2.1. - CLD - Mean Estimate Differences to Control

Compact Letter Display of estimates for Dry Root Mean weight (mg) differences between plants that were inoculated with a mucilage isolates and the non-inoculated control.

```{r}
plot(CLD(lsmeans(lm_set_4.2_DR, trt.vs.ctrl~isolate,ref=24)$contrasts, alpha=0.1), main="Mean Dry Root Weight Differences of Inoculated Plants vs. Control", xlab = "Difference of Mean Estimates (mg)", ylab = "Contrasts")
```

> BCW201815 and BCW201084 inoculated plants had higher mean dry root weights than the control, and the confidence intervals for these estimates do not overlap with those of the control mean.

### 4.2.2. Dry Shoot Weight

```{r, fig.width=10}
# linear model of dry shoot data
lm_set_4.2_DS <- lm(mg ~ 1 + isolate, set_4.2_DS)
op = par(mfrow=c(1,2))
plot(lm_set_4.2_DS, which = c(2,3))
par(op)
```

#### 4.2.2. - Anova

```{r}
# assess variance
anova(lm_set_4.2_DS)
```

#### 4.2.2. - LSMeans - Dunnett

```{r}
lsm_s4.2.dun.DS <- summary(lsmeans(lm_set_4.2_DS, trt.vs.ctrl ~isolate, ref=24)$contrasts, infer = c(T,T))
write.csv(lsm_s4.2.dun.DS, "./lsmeans_summary_tables/lsm_s4.2.dun.DS.csv")
```

#### 4.2.2. - CLD - (Pairwise by isolate)

```{r}
CLD(lsmeans(lm_set_4.2_DS, ~isolate), alpha=0.1)
plot(CLD(lsmeans(lm_set_4.2_DS, ~isolate), alpha=0.1), main="All pairwise comparisons for Mean Dry Shoot Weight", xlab = "Mean Weight (mg)", ylab = "Isolate ID")
```

#### 4.2.2. - CLD - Mean Estimate Differences to Control

Compact Letter Display of estimates for Dry Shoot Mean weight (mg) differences between plants that were inoculated with a mucilage isolates and the non-inoculated control.

```{r}
plot(CLD(lsmeans(lm_set_4.2_DS, trt.vs.ctrl~isolate,ref=24)$contrasts, alpha=0.1), main="Mean Dry Shoot Weight Differences of Inoculated Plants vs. Control", xlab = "Difference of Mean Estimates (mg)", ylab = "Contrasts")
```

> No significant effect observed for dry shoot weight of batch 4.2 at an alpha level of 0.1, but `BCW201815` is very close to having non-overlapping CI for the mean estimate.

### 4.2.3. Wet Root Weight

```{r, fig.width=10}
# linear model of wet root data
lm_set_4.2_WR <- lm(mg ~ 1 + isolate, set_4.2_WR)
op = par(mfrow=c(1,2))
plot(lm_set_4.2_WR, which = c(2,3))
par(op)
```

#### 4.2.3. - Anova

```{r}
# assess variance
anova(lm_set_4.2_WR)
```

#### 4.2.3. - LSMeans - Dunnett

```{r}
lsm_s4.2.dun.WR <- summary(lsmeans(lm_set_4.2_WR, trt.vs.ctrl ~isolate, ref=24)$contrasts, infer = c(T,T))
write.csv(lsm_s4.2.dun.WR, "./lsmeans_summary_tables/lsm_s4.2.dun.WR.csv")
```

#### 4.2.3. - CLD - (Pairwise by isolate)

```{r}
CLD(lsmeans(lm_set_4.2_WR, ~isolate), alpha=0.1)
plot(CLD(lsmeans(lm_set_4.2_WR, ~isolate), alpha=0.1), main="All pairwise comparisons for Mean Wet Root Weight", xlab = "Mean Weight (mg)", ylab = "Isolate ID")
```

#### 4.2.3. - CLD - Mean Estimate Differences to Control

Compact Letter Display of estimates for Wet Root Mean weight (mg) differences between plants that were inoculated with a mucilage isolates and the non-inoculated control.

```{r}
plot(CLD(lsmeans(lm_set_4.2_WR, trt.vs.ctrl~isolate,ref=24)$contrasts, alpha=0.1), main="Mean Wet Root Weight Differences of Inoculated Plants vs. Control", xlab = "Difference of Mean Estimates (mg)", ylab = "Contrasts")
```

> `BCW201084` and `BCW201815` both have mean wet root weight estimates higher than the control and the confidence intervals for these two isolate's mean estimates do not overlap with those of the control.

### 4.2.4. Wet Shoot Weight

```{r, fig.width=10}
# linear model of wet shoot data
lm_set_4.2_WS <- lm(mg ~ 1 + isolate, set_4.2_WS)
op = par(mfrow=c(1,2))
plot(lm_set_4.2_WS, which = c(2,3))
par(op)
```

#### 4.2.4. - Anova

```{r}
# assess variance
anova(lm_set_4.2_WS)
```

#### 4.2.4. - LSMeans - Dunnett

```{r}
lsm_s4.2.dun.WS <- summary(lsmeans(lm_set_4.2_WS, trt.vs.ctrl ~isolate, ref=24)$contrasts, infer = c(T,T))
write.csv(lsm_s4.2.dun.WS, "./lsmeans_summary_tables/lsm_s4.2.dun.WS.csv")
```

#### 4.2.4. - CLD - (Pairwise by isolate)

```{r}
CLD(lsmeans(lm_set_4.2_WS, ~isolate), alpha=0.1)
plot(CLD(lsmeans(lm_set_4.2_WS, ~isolate), alpha=0.1), main="All pairwise comparisons for Mean Wet Shoot Weight", xlab = "Mean Weight (mg)", ylab = "Isolate ID")
```

#### 4.2.4. - CLD - Mean Estimate Differences to Control

Compact Letter Display of estimates for Wet Shoot Mean weight (mg) differences between plants that were inoculated with a mucilage isolates and the non-inoculated control.

```{r}
plot(CLD(lsmeans(lm_set_4.2_WS, trt.vs.ctrl~isolate,ref=24)$contrasts, alpha=0.1), main="Mean Wet Shoot Weight Differences of Inoculated Plants vs. Control", xlab = "Difference of Mean Estimates (mg)", ylab = "Contrasts")
```

> There are no positive effects observed based on measurements of wet shoot weight for plants inoculated with isolates in batch 4.2 that have non-overlapping confidence intervals with those of the control.

## Batch 4.3.

```{r}
# analyze set 4.3 data
str(set_4.3)

#subset by sample type

set_4.3_DR <- filter(set_4.3, sample == "DR")
set_4.3_DS <- filter(set_4.3, sample == "DS")
set_4.3_WR <- filter(set_4.3, sample == "WR")
set_4.3_WS <- filter(set_4.3, sample == "WS")

```

**Experimental Design Table**
  
  | Structure | Factor | Type | # levels |
  |-----------|--------|------|----------|
  | Treatment | Isolate | Qualitative | 30 |
  | Design    | MS Media Slants | Experimental Unit | 90 |
  | Response  | Plant Tissue Weight | Observational Unit | 360 |
  |Response | Dry Root Weight (DR) | Variable | 90 | 
  | Response | Dry Shoot Weight (DS) | Variable | 90 |
  | Response | Wet Root Weight (WR) | Variable | 90 | 
  | Response | Wet Shoot Weight (WS) | Variable | 90 |

### 4.3.1. Dry Root Weight

```{r, fig.width=10}
# linear model of dry root data
lm_set_4.3_DR <- lm(mg ~ 1 + isolate, set_4.3_DR)
op = par(mfrow=c(1,2))
plot(lm_set_4.3_DR, which = c(2,3))
par(op)
```

#### 4.3.1. - Anova

```{r}
# assess variance
anova(lm_set_4.3_DR)
```

#### 4.3.1. - LSMeans - Dunnett

```{r}
lsm_s4.3.dun.DR <- summary(lsmeans(lm_set_4.3_DR, trt.vs.ctrl ~isolate, ref=25)$contrasts, infer = c(T,T))
write.csv(lsm_s4.3.dun.DR, "./lsmeans_summary_tables/lsm_s4.3.dun.DR.csv")
```

#### 4.3.1. - CLD - (Pairwise by isolate)

```{r}
CLD(lsmeans(lm_set_4.3_DR, ~isolate), alpha=0.1)
plot(CLD(lsmeans(lm_set_4.3_DR, ~isolate), alpha=0.1), main="All pairwise comparisons for Mean Dry Root Weight", xlab = "Mean Weight (mg)", ylab = "Isolate ID")
```

#### 4.3.1. - CLD - Mean Estimate Differences to Control

Compact Letter Display of estimates for Dry Root Mean weight (mg) differences between plants that were inoculated with a mucilage isolates and the non-inoculated control.

```{r}
plot(CLD(lsmeans(lm_set_4.3_DR, trt.vs.ctrl~isolate,ref=25)$contrasts, alpha=0.1), main="Mean Dry Root Weight Differences of Inoculated Plants vs. Control", xlab = "Difference of Mean Estimates (mg)", ylab = "Contrasts")
```

> No significant effect observed for dry root measurements in batch 4.3.

### 4.3.2. Dry Shoot Weight

```{r, fig.width=10}
# linear model of dry shoot data
lm_set_4.3_DS <- lm(mg ~ 1 + isolate, set_4.3_DS)
op = par(mfrow=c(1,2))
plot(lm_set_4.3_DS, which = c(2,3))
par(op)
```

#### 4.3.2. - Anova

```{r}
# assess variance
anova(lm_set_4.3_DS)
```

#### 4.3.2. - LSMeans - Dunnett

```{r}
lsm_s4.3.dun.DS <- summary(lsmeans(lm_set_4.3_DS, trt.vs.ctrl ~isolate, ref=25)$contrasts, infer = c(T,T))
write.csv(lsm_s4.3.dun.DS, "./lsmeans_summary_tables/lsm_s4.3.dun.DS.csv")
```

#### 4.3.2. - CLD - (Pairwise by isolate)

```{r}
CLD(lsmeans(lm_set_4.3_DS, ~isolate), alpha=0.1)
plot(CLD(lsmeans(lm_set_4.3_DS, ~isolate), alpha=0.1), main="All pairwise comparisons for Mean Dry Shoot Weight", xlab = "Mean Weight (mg)", ylab = "Isolate ID")
```

#### 4.3.2. - CLD - Mean Estimate Differences to Control

Compact Letter Display of estimates for Dry Shoot Mean weight (mg) differences between plants that were inoculated with a mucilage isolates and the non-inoculated control.

```{r}
plot(CLD(lsmeans(lm_set_4.3_DS, trt.vs.ctrl~isolate,ref=25)$contrasts, alpha=0.1), main="Mean Dry Shoot Weight Differences of Inoculated Plants vs. Control", xlab = "Difference of Mean Estimates (mg)", ylab = "Contrasts")
```

> No significant effect observed for dry shoot measurements in batch 4.3.

### 4.3.3. Wet Root Weight

```{r, fig.width=10}
# linear model of wet root data
lm_set_4.3_WR <- lm(mg ~ 1 + isolate, set_4.3_WR)
op = par(mfrow=c(1,2))
plot(lm_set_4.3_WR, which = c(2,3))
par(op)
```

#### 4.3.3. - Anova

```{r}
# assess variance
anova(lm_set_4.3_WR)
```

#### 4.3.3. - LSMeans - Dunnett

```{r}
lsm_s4.3.dun.WR <- summary(lsmeans(lm_set_4.3_WR, trt.vs.ctrl ~isolate, ref=25)$contrasts, infer = c(T,T))
write.csv(lsm_s4.3.dun.WR, "./lsmeans_summary_tables/lsm_s4.3.dun.WR.csv")
```

#### 4.3.3. - CLD - (Pairwise by isolate)

```{r}
CLD(lsmeans(lm_set_4.3_WR, ~isolate), alpha=0.1)
plot(CLD(lsmeans(lm_set_4.3_WR, ~isolate), alpha=0.1), main="All pairwise comparisons for Mean Wet Root Weight", xlab = "Mean Weight (mg)", ylab = "Isolate ID")
```

#### 4.3.3. - CLD - Mean Estimate Differences to Control

Compact Letter Display of estimates for Wet Root Mean weight (mg) differences between plants that were inoculated with a mucilage isolates and the non-inoculated control.

```{r}
plot(CLD(lsmeans(lm_set_4.3_WR, trt.vs.ctrl~isolate,ref=25)$contrasts, alpha=0.1), main="Mean Wet Root Weight Differences of Inoculated Plants vs. Control", xlab = "Difference of Mean Estimates (mg)", ylab = "Contrasts")
```

> No significant positive effect observed for wet root measurements in batch 4.3.

### 4.3.4. Wet Shoot Weight

```{r, fig.width=10}
# linear model of wet shoot data
lm_set_4.3_WS <- lm(mg ~ 1 + isolate, set_4.3_WS)
op = par(mfrow=c(1,2))
plot(lm_set_4.3_WS, which = c(2,3))
par(op)
```

#### 4.3.4. - Anova

```{r}
# assess variance
anova(lm_set_4.3_WS)
```

#### 4.3.4. - LSMeans - Dunnett

```{r}
lsm_s4.3.dun.WS <- summary(lsmeans(lm_set_4.3_WS, trt.vs.ctrl ~isolate, ref=25)$contrasts, infer = c(T,T))
write.csv(lsm_s4.3.dun.WS, "./lsmeans_summary_tables/lsm_s4.3.dun.WS.csv")
```

#### 4.3.4. - CLD - (Pairwise by isolate)

```{r}
CLD(lsmeans(lm_set_4.3_WS, ~isolate), alpha=0.1)
plot(CLD(lsmeans(lm_set_4.3_WS, ~isolate), alpha=0.1), main="All pairwise comparisons for Mean Wet Shoot Weight", xlab = "Mean Weight (mg)", ylab = "Isolate ID")
```

#### 4.3.4. - CLD - Mean Estimate Differences to Control

Compact Letter Display of estimates for Wet Shoot Mean weight (mg) differences between plants that were inoculated with a mucilage isolates and the non-inoculated control.

```{r}
plot(CLD(lsmeans(lm_set_4.3_WS, trt.vs.ctrl~isolate,ref=25)$contrasts, alpha=0.1), main="Mean Wet Shoot Weight Differences of Inoculated Plants vs. Control", xlab = "Difference of Mean Estimates (mg)", ylab = "Contrasts")
```

> No significant positive effect observed for inoculation treatments on wet shoot measurements in batch 4.3.




## Summary

> The isolates below were each found to have statistically significant differences in mean weight for the specified response variables that were measured, where inclusion in the summary table was contingent upon the confidence intervals for the estimates being non-overlapping with those of the experimental control group (alpha level of 0.1 with correction for multiple testing).  

| Isolate List | Batch | Response | Difference from Control (mg) |
|--------------|-------|-----------|-----------------------------|
| BCW201858    |  1    | Dry Root  |             9.70            |
| BCW200727    |  1    | Dry Shoot |            21.08            |
| BCW200556    |  1    | Wet Root  |            95.85            |
| BCW200533    |  1    | Wet Root  |            93.01            |
| BCW200810    |  2.1  | Dry Shoot |            21.75            |
| BCW200810    |  2.1  | Wet Root  |            60.9             |
| BCW201873    |  2.1  | Wet Root  |            70.43            |
| BCW201809    |  2.1  | Wet Root  |            62.01            |
| BCW201864    |  2.1  | Wet Root  |            65.43            |
| BCW201849    |  2.2  | Dry Root  |             5.02            |
| BCW201849    |  2.2  | Dry Shoot |            29.35            |
| BCW201849    |  2.2  | Wet Shoot |           415.22            |
| BCW201881    |  2.3  | Dry Root  |            11.94            |
| BCW201900    |  2.4  | Dry Root  |            5.63             |
| BCW201900    |  2.4  | Dry Shoot |            25.92            |
| BCW201814    |  2.4  | Dry Shoot |            24.19            |
| BCW201900    |  2.4  | Wet Root  |            79.7             |
| BCW201814    |  2.4  | Wet Shoot |           248.28            |
| BCW201900    |  2.4  | Wet Shoot |           397.05            |
| BCW201088    |  3.1  | Dry Root  |             3.90            |
| BCW201914    |  3.1  | Dry Shoot |            20.23            |
| BCW201815    |  4.2  | Dry Root  |             2.67            |
| BCW201084    |  4.2  | Dry Root  |             2.43            |
| BCW201815    |  4.2  | Dry Shoot |            21.10            |
| BCW201084    |  4.2  | Wet Root  |            28.90            |
| BCW201815    |  4.2  | Wet Root  |            27.27            |

**Total Number of isolates with statistically supported positive effects on mean weight:** 16
**Note:** There are several isolates in the first batch that display the trend of highly increased plant biomass, either in shoots or roots, but the confidence intervals overlap with those of the controls based on linear modeling of the data.

### N isolates in each batch
```{r}
str(set_1)
unique(set_1$isolate)
```

